1
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Park SL, Christo SN, Wells AC, Gandolfo LC, Zaid A, Alexandre YO, Burn TN, Schröder J, Collins N, Han SJ, Guillaume SM, Evrard M, Castellucci C, Davies B, Osman M, Obers A, McDonald KM, Wang H, Mueller SN, Kannourakis G, Berzins SP, Mielke LA, Carbone FR, Kallies A, Speed TP, Belkaid Y, Mackay LK. Divergent molecular networks program functionally distinct CD8 + skin-resident memory T cells. Science 2023; 382:1073-1079. [PMID: 38033053 DOI: 10.1126/science.adi8885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023]
Abstract
Skin-resident CD8+ T cells include distinct interferon-γ-producing [tissue-resident memory T type 1 (TRM1)] and interleukin-17 (IL-17)-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations use common mechanisms to establish tissue residence is unknown. In this work, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in the skin. TRM1 cells depend on a T-bet-Hobit-IL-15 axis, whereas TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, by targeting this pathway, skin TRM17 cells can be ablated without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.
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Affiliation(s)
- Simone L Park
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Susan N Christo
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Alexandria C Wells
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
| | - Luke C Gandolfo
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia
- Walter and Eliza Hall Institute for Medical Research, Parkville, VIC, Australia
| | - Ali Zaid
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Yannick O Alexandre
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Thomas N Burn
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Jan Schröder
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Nicholas Collins
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
| | - Seong-Ji Han
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
| | - Stéphane M Guillaume
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Maximilien Evrard
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Clara Castellucci
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Brooke Davies
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Maleika Osman
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Andreas Obers
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Keely M McDonald
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Huimeng Wang
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Scott N Mueller
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - George Kannourakis
- Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia
- Fiona Elsey Cancer Research Institute, Ballarat, VIC, Australia
| | - Stuart P Berzins
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, Australia
- Fiona Elsey Cancer Research Institute, Ballarat, VIC, Australia
| | - Lisa A Mielke
- Olivia Newton-John Cancer Research Institute, La Trobe University School of Cancer Medicine, Heidelberg, VIC, Australia
| | - Francis R Carbone
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Axel Kallies
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Terence P Speed
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia
- Walter and Eliza Hall Institute for Medical Research, Parkville, VIC, Australia
| | - Yasmine Belkaid
- Metaorganism Immunity Section, Laboratory of Host Immunity and Microbiome, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA
- NIAID Microbiome Program, NIAID, National Institutes of Health, Bethesda, MD, USA
| | - Laura K Mackay
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
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2
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Caramia F, Speed TP, Shen H, Haupt Y, Haupt S. Establishing the Link between X-Chromosome Aberrations and TP53 Status, with Breast Cancer Patient Outcomes. Cells 2023; 12:2245. [PMID: 37759468 PMCID: PMC10526523 DOI: 10.3390/cells12182245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/04/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Ubiquitous to normal female human somatic cells, X-chromosome inactivation (XCI) tightly regulates the transcriptional silencing of a single X chromosome from each pair. Some genes escape XCI, including crucial tumour suppressors. Cancer susceptibility can be influenced by the variability in the genes that escape XCI. The mechanisms of XCI dysregulation remain poorly understood in complex diseases, including cancer. Using publicly available breast cancer next-generation sequencing data, we show that the status of the major tumour suppressor TP53 from Chromosome 17 is highly associated with the genomic integrity of the inactive X (Xi) and the active X (Xa) chromosomes. Our quantification of XCI and XCI escape demonstrates that aberrant XCI is linked to poor survival. We derived prognostic gene expression signatures associated with either large deletions of Xi; large amplifications of Xa; or abnormal X-methylation. Our findings expose a novel insight into female cancer risks, beyond those associated with the standard molecular subtypes.
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Affiliation(s)
- Franco Caramia
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; (F.C.); (Y.H.)
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Terence P. Speed
- Walter and Eliza Hall Institute for Medical Research, Parkville, VIC 3052, Australia;
| | - Hui Shen
- Van Andel Institute, Grand Rapids, MI 49503, USA;
| | - Ygal Haupt
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; (F.C.); (Y.H.)
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Sue Haupt
- Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia; (F.C.); (Y.H.)
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC 3010, Australia
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3
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Mangiola S, Roth-Schulze AJ, Trussart M, Zozaya-Valdés E, Ma M, Gao Z, Rubin AF, Speed TP, Shim H, Papenfuss AT. sccomp: Robust differential composition and variability analysis for single-cell data. Proc Natl Acad Sci U S A 2023; 120:e2203828120. [PMID: 37549298 PMCID: PMC10438834 DOI: 10.1073/pnas.2203828120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 05/18/2023] [Indexed: 08/09/2023] Open
Abstract
Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.
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Affiliation(s)
- Stefano Mangiola
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Alexandra J. Roth-Schulze
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Marie Trussart
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
| | - Enrique Zozaya-Valdés
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Mengyao Ma
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
| | - Zijie Gao
- Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC3052, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC3052, Australia
| | - Alan F. Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Terence P. Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
| | - Heejung Shim
- Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC3052, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC3052, Australia
| | - Anthony T. Papenfuss
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
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4
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Gurun B, Horton W, Murugan D, Zhu B, Leyshock P, Kumar S, Byrne KT, Vonderheide RH, Margolin AA, Mori M, Spellman PT, Coussens LM, Speed TP. An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences. BMC Genomics 2023; 24:349. [PMID: 37365517 DOI: 10.1186/s12864-023-09424-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 05/31/2023] [Indexed: 06/28/2023] Open
Abstract
T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.
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Affiliation(s)
- Burcu Gurun
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
- School of Medicine, Oregon Health and Science University, Portland, OR, USA.
| | - Wesley Horton
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Dhaarini Murugan
- Department of Cell, Developmental & Cancer Biology and Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Biqing Zhu
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Patrick Leyshock
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Sushil Kumar
- Department of Cell, Developmental & Cancer Biology and Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Katelyn T Byrne
- Department of Cell, Developmental & Cancer Biology and Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert H Vonderheide
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Motomi Mori
- Department of Biostatistics, St. Jude's Children's Research Hospital, Memphis, TN, USA
| | - Paul T Spellman
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
| | - Lisa M Coussens
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
- Department of Cell, Developmental & Cancer Biology and Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, 3010, Australia.
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5
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Feng Y, Yang T, Zhu J, Li M, Doyle M, Ozcoban V, Bass GT, Pizzolla A, Cain L, Weng S, Pasam A, Kocovski N, Huang YK, Keam SP, Speed TP, Neeson PJ, Pearson RB, Sandhu S, Goode DL, Trigos AS. Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments. Nat Commun 2023; 14:2697. [PMID: 37188662 DOI: 10.1038/s41467-023-37822-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.
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Affiliation(s)
- Yuzhou Feng
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Tianpei Yang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - John Zhu
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mabel Li
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Maria Doyle
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Volkan Ozcoban
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Greg T Bass
- Research & Development, CSL Innovation, Parkville, VIC, Australia
| | - Angela Pizzolla
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Lachlan Cain
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Sirui Weng
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Anupama Pasam
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | | | - Yu-Kuan Huang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Simon P Keam
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Paul J Neeson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Richard B Pearson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Shahneen Sandhu
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - David L Goode
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Anna S Trigos
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
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6
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Weeden CE, Gayevskiy V, Marceaux C, Batey D, Tan T, Yokote K, Ribera NT, Clatch A, Christo S, Teh CE, Mitchell AJ, Trussart M, Rankin LC, Obers A, McDonald JA, Sutherland KD, Sharma VJ, Starkey G, D'Costa R, Antippa P, Leong T, Steinfort D, Irving L, Swanton C, Gordon CL, Mackay LK, Speed TP, Gray DHD, Asselin-Labat ML. Early immune pressure initiated by tissue-resident memory T cells sculpts tumor evolution in non-small cell lung cancer. Cancer Cell 2023; 41:837-852.e6. [PMID: 37086716 DOI: 10.1016/j.ccell.2023.03.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/05/2023] [Accepted: 03/24/2023] [Indexed: 04/24/2023]
Abstract
Tissue-resident memory T (TRM) cells provide immune defense against local infection and can inhibit cancer progression. However, it is unclear to what extent chronic inflammation impacts TRM activation and whether TRM cells existing in tissues before tumor onset influence cancer evolution in humans. We performed deep profiling of healthy lungs and lung cancers in never-smokers (NSs) and ever-smokers (ESs), finding evidence of enhanced immunosurveillance by cells with a TRM-like phenotype in ES lungs. In preclinical models, tumor-specific or bystander TRM-like cells present prior to tumor onset boosted immune cell recruitment, causing tumor immune evasion through loss of MHC class I protein expression and resistance to immune checkpoint inhibitors. In humans, only tumors arising in ES patients underwent clonal immune evasion, unrelated to tobacco-associated mutagenic signatures or oncogenic drivers. These data demonstrate that enhanced TRM-like activity prior to tumor development shapes the evolution of tumor immunogenicity and can impact immunotherapy outcomes.
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Affiliation(s)
- Clare E Weeden
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Velimir Gayevskiy
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Claire Marceaux
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Daniel Batey
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Tania Tan
- Immunology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Kenta Yokote
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Nina Tubau Ribera
- Advanced Technology and Biology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Allison Clatch
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Susan Christo
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Charis E Teh
- Immunology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Andrew J Mitchell
- Materials Characterisation and Fabrication Platform, Department of Chemical Engineering, the University of Melbourne, Parkville, VIC, Australia
| | - Marie Trussart
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Lucille C Rankin
- Immunology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Andreas Obers
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Jackson A McDonald
- ACRF Stem Cells and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Kate D Sutherland
- ACRF Stem Cells and Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia
| | - Varun J Sharma
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia; Liver and Intestinal Transplant Unit, Austin Health, Heidelberg, VIC, Australia; Department of Cardiothoracic Surgery, Austin Health, Heidelberg, VIC, Australia
| | - Graham Starkey
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia; Liver and Intestinal Transplant Unit, Austin Health, Heidelberg, VIC, Australia
| | - Rohit D'Costa
- DonateLife Victoria, Carlton, VIC, Australia; Department of Intensive Care Medicine, Melbourne Health, Melbourne, VIC, Australia
| | - Phillip Antippa
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia; The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Tracy Leong
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia; Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, VIC, Australia
| | - Daniel Steinfort
- Department of Medicine, the University of Melbourne, Parkville, VIC, Australia; The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Louis Irving
- Department of Medicine, the University of Melbourne, Parkville, VIC, Australia; The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London, UK; Department of Oncology, University College London Hospitals, London, UK
| | - Claire L Gordon
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia; Department of Infectious Diseases, Austin Health, Heidelberg, VIC, Australia; North Eastern Public Health Unit, Austin Health, Heidelberg, VIC, Australia
| | - Laura K Mackay
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; School of Mathematics and Statistics, the University of Melbourne, Parkville, VIC, Australia
| | - Daniel H D Gray
- Immunology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia.
| | - Marie-Liesse Asselin-Labat
- Personalised Oncology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia; Department of Medical Biology, the University of Melbourne, Parkville, VIC, Australia.
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7
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Luen SJ, Viale G, Nik-Zainal S, Savas P, Kammler R, Dell'Orto P, Biasi O, Degasperi A, Brown LC, Láng I, MacGrogan G, Tondini C, Bellet M, Villa F, Bernardo A, Ciruelos E, Karlsson P, Neven P, Climent M, Müller B, Jochum W, Bonnefoi H, Martino S, Davidson NE, Geyer C, Chia SK, Ingle JN, Coleman R, Solbach C, Thürlimann B, Colleoni M, Coates AS, Goldhirsch A, Fleming GF, Francis PA, Speed TP, Regan MM, Loi S. Genomic characterisation of hormone receptor-positive breast cancer arising in very young women. Ann Oncol 2023; 34:397-409. [PMID: 36709040 PMCID: PMC10619213 DOI: 10.1016/j.annonc.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/14/2022] [Accepted: 01/15/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Very young premenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+HER2-) early breast cancer (EBC) have higher rates of recurrence and death for reasons that remain largely unexplained. PATIENTS AND METHODS Genomic sequencing was applied to HR+HER2- tumours from patients enrolled in the Suppression of Ovarian Function Trial (SOFT) to determine genomic drivers that are enriched in young premenopausal women. Genomic alterations were characterised using next-generation sequencing from a subset of 1276 patients (deep targeted sequencing, n = 1258; whole-exome sequencing in a young-age, case-control subsample, n = 82). We defined copy number (CN) subgroups and assessed for features suggestive of homologous recombination deficiency (HRD). Genomic alteration frequencies were compared between young premenopausal women (<40 years) and older premenopausal women (≥40 years), and assessed for associations with distant recurrence-free interval (DRFI) and overall survival (OS). RESULTS Younger women (<40 years, n = 359) compared with older women (≥40 years, n = 917) had significantly higher frequencies of mutations in GATA3 (19% versus 16%) and CN amplifications (CNAs) (47% versus 26%), but significantly lower frequencies of mutations in PIK3CA (32% versus 47%), CDH1 (3% versus 9%), and MAP3K1 (7% versus 12%). Additionally, they had significantly higher frequencies of features suggestive of HRD (27% versus 21%) and a higher proportion of PIK3CA mutations with concurrent CNAs (23% versus 11%). Genomic features suggestive of HRD, PIK3CA mutations with CNAs, and CNAs were associated with significantly worse DRFI and OS compared with those without these features. These poor prognostic features were enriched in younger patients: present in 72% of patients aged <35 years, 54% aged 35-39 years, and 40% aged ≥40 years. Poor prognostic features [n = 584 (46%)] versus none [n = 692 (54%)] had an 8-year DRFI of 84% versus 94% and OS of 88% versus 96%. Younger women (<40 years) had the poorest outcomes: 8-year DRFI 74% versus 85% and OS 80% versus 93%, respectively. CONCLUSION These results provide insights into genomic alterations that are enriched in young women with HR+HER2- EBC, provide rationale for genomic subgrouping, and highlight priority molecular targets for future clinical trials.
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Affiliation(s)
- S J Luen
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
| | - G Viale
- International Breast Cancer Study Group Central Pathology Office, IEO European Institute of Oncology IRCCS, University of Milan, Milan, Italy
| | - S Nik-Zainal
- Department of Medical Genetics & MRC Cancer Unit, The Clinical School, University of Cambridge, Cambridge, UK
| | - P Savas
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
| | - R Kammler
- International Breast Cancer Study Group, Coordinating Center, Central Pathology Office, Bern, Switzerland
| | - P Dell'Orto
- International Breast Cancer Study Group Central Pathology Office, Department of Pathology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - O Biasi
- Division of Pathology and Laboratory Medicine, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - A Degasperi
- Department of Medical Genetics & MRC Cancer Unit, The Clinical School, University of Cambridge, Cambridge, UK
| | - L C Brown
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
| | - I Láng
- Istenhegyi Health Center Oncology Clinic, National Institute of Oncology, Budapest, Hungary
| | - G MacGrogan
- Biopathology Department, Institut Bergonié Comprehensive Cancer Centre, Bordeaux, France
| | - C Tondini
- Osp. Papa Giovanni XXIII, Bergamo, Italy
| | - M Bellet
- Vall d'Hebron Institute of Oncology (VHIO) and Vall d'Hebron University Hospital, Barcelona, Spain
| | - F Villa
- Oncology Unit, Department of Oncology, Alessandro Manzoni Hospital, ASST Lecco, Lecco, Italy
| | - A Bernardo
- ICS Maugeri IRCCS, Medical Oncology Unit of Pavia Institute, Italy
| | - E Ciruelos
- University Hospital 12 de Octubre, Madrid, Spain
| | - P Karlsson
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - P Neven
- Gynecologic Oncology and Multidisciplinary Breast Center, University Hospitals UZ-Leuven, KU Leuven, Leuven, Belgium
| | - M Climent
- Instituto Valenciano de Oncologia, Valencia, Spain
| | - B Müller
- Chilean Cooperative Group for Oncologic Research (GOCCHI), Santiago, Chile
| | - W Jochum
- Institute of Pathology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland; Swiss Group for Clinical Cancer Research (SAKK), Berne, Switzerland
| | - H Bonnefoi
- Institut Bergonié Comprehensive Cancer Centre, Université de Bordeaux, INSERM U1218, Bordeaux, France; European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - S Martino
- The Angeles Clinic and Research Institute, Santa Monica, USA
| | - N E Davidson
- Fred Hutchinson Cancer Research Center, University of Washington, Seattle, USA
| | - C Geyer
- Houston Methodist Cancer Center, NRG Oncology, Houston, USA
| | - S K Chia
- BC Cancer and Canadian Cancer Trials Group, Vancouver, Canada
| | - J N Ingle
- Mayo Clinic, Rochester, Minnesota, USA
| | - R Coleman
- National Institute for Health Research (NIHR) Cancer Research Network, University of Sheffield, Sheffield, UK
| | - C Solbach
- Breast Center, University Hospital, Goethe University Frankfurt, Frankfurt, Germany
| | - B Thürlimann
- Swiss Group for Clinical Cancer Research (SAKK), Berne, Switzerland; Breast Center, Kantonsspital, St. Gallen, Switzerland
| | - M Colleoni
- Division of Medical Senology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - A S Coates
- International Breast Cancer Study Group and University of Sydney, Sydney, Australia
| | - A Goldhirsch
- International Breast Cancer Study Group (IBCSG), Bern Switzerland and IEO European Institute of Oncology IRCCS, Milan, Italy
| | - G F Fleming
- Section of Hematology Oncology, The University of Chicago, Chicago, USA
| | - P A Francis
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia
| | - T P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute, Melbourne, Australia
| | - M M Regan
- Division of Biostatistics, International Breast Cancer Study Group Statistical Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - S Loi
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Australia.
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8
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Virassamy B, Caramia F, Savas P, Sant S, Wang J, Christo SN, Byrne A, Clarke K, Brown E, Teo ZL, von Scheidt B, Freestone D, Gandolfo LC, Weber K, Teply-Szymanski J, Li R, Luen SJ, Denkert C, Loibl S, Lucas O, Swanton C, Speed TP, Darcy PK, Neeson PJ, Mackay LK, Loi S. Intratumoral CD8 + T cells with a tissue-resident memory phenotype mediate local immunity and immune checkpoint responses in breast cancer. Cancer Cell 2023; 41:585-601.e8. [PMID: 36827978 DOI: 10.1016/j.ccell.2023.01.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 10/17/2022] [Accepted: 01/13/2023] [Indexed: 02/25/2023]
Abstract
CD8+ tumor-infiltrating lymphocytes with a tissue-resident memory T (TRM) cell phenotype are associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, the relative contribution of CD8+ TRM cells to anti-tumor immunity and immune checkpoint blockade efficacy in breast cancer remains unknown. Here, we show that intratumoral CD8+ T cells in murine mammary tumors transcriptionally resemble those from TNBC patients. Phenotypic and transcriptional studies established two intratumoral sub-populations: one more enriched in markers of terminal exhaustion (TEX-like) and the other with a bona fide resident phenotype (TRM-like). Treatment with anti-PD-1 and anti-CTLA-4 therapy resulted in expansion of these intratumoral populations, with the TRM-like subset displaying significantly enhanced cytotoxic capacity. TRM-like CD8+ T cells could also provide local immune protection against tumor rechallenge and a TRM gene signature extracted from tumor-free tissue was significantly associated with improved clinical outcomes in TNBC patients treated with checkpoint inhibitors.
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Affiliation(s)
- Balaji Virassamy
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Franco Caramia
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Peter Savas
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sneha Sant
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Jianan Wang
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia
| | - Susan N Christo
- Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Ann Byrne
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Kylie Clarke
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Emmaline Brown
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Zhi Ling Teo
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Bianca von Scheidt
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David Freestone
- Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Luke C Gandolfo
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Karsten Weber
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany
| | - Julia Teply-Szymanski
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany; Department of Pathology, University Marburg-Giessen, Campus Marburg, Germany
| | - Ran Li
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Stephen J Luen
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Carsten Denkert
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany; Department of Pathology, University Marburg-Giessen, Campus Marburg, Germany
| | - Sibylle Loibl
- German Breast Cancer Group, GBG-Forschungs GmbH, Neu-Isenburg, Germany
| | - Olivia Lucas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK; Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Phillip K Darcy
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia.
| | - Paul J Neeson
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia.
| | - Laura K Mackay
- Department of Medical Biology, The University of Melbourne, Melbourne, VIC, Australia; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
| | - Sherene Loi
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, VIC, Australia.
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9
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Gurun B, Horton W, Murugan D, Zhu B, Leyshock P, Kumar S, Byrne KT, Vonderheide RH, Margolin AA, Mori M, Spellman PT, Coussens LM, Speed TP. An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences. Res Sq 2023:rs.3.rs-2140339. [PMID: 36824803 PMCID: PMC9949261 DOI: 10.21203/rs.3.rs-2140339/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.
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10
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Lönnstedt IM, Speed TP. RUV
retrieves unknown experimental designs. Scand Stat Theory Appl 2023. [DOI: 10.1111/sjos.12633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ingrid M. Lönnstedt
- Bioinformatics Division Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia
- SDS Life Science ‐ a Cytel company Biostatistics group Uppsala Sweden
| | - Terence P. Speed
- Bioinformatics Division Walter and Eliza Hall Institute of Medical Research Parkville VIC Australia
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11
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Molania R, Foroutan M, Gagnon-Bartsch JA, Gandolfo LC, Jain A, Sinha A, Olshansky G, Dobrovic A, Papenfuss AT, Speed TP. Removing unwanted variation from large-scale RNA sequencing data with PRPS. Nat Biotechnol 2023; 41:82-95. [PMID: 36109686 PMCID: PMC9849124 DOI: 10.1038/s41587-022-01440-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 06/30/2022] [Indexed: 01/22/2023]
Abstract
Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.
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Affiliation(s)
- Ramyar Molania
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Momeneh Foroutan
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
| | | | - Luke C Gandolfo
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia
| | - Aryan Jain
- Department of Economics and Statistics, Monash University, Melbourne, Victoria, Australia
| | - Abhishek Sinha
- Department of Economics and Statistics, Monash University, Melbourne, Victoria, Australia
| | - Gavriel Olshansky
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alexander Dobrovic
- Department of Surgery, The University of Melbourne, Austin Health, Heidelberg, Victoria, Australia
| | - Anthony T Papenfuss
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia.
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Terence P Speed
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia.
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12
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Fonseca R, Burn TN, Gandolfo LC, Devi S, Park SL, Obers A, Evrard M, Christo SN, Buquicchio FA, Lareau CA, McDonald KM, Sandford SK, Zamudio NM, Zanluqui NG, Zaid A, Speed TP, Satpathy AT, Mueller SN, Carbone FR, Mackay LK. Runx3 drives a CD8 + T cell tissue residency program that is absent in CD4 + T cells. Nat Immunol 2022; 23:1236-1245. [PMID: 35882933 DOI: 10.1038/s41590-022-01273-4] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/17/2022] [Indexed: 11/09/2022]
Abstract
Tissue-resident memory T cells (TRM cells) provide rapid and superior control of localized infections. While the transcription factor Runx3 is a critical regulator of CD8+ T cell tissue residency, its expression is repressed in CD4+ T cells. Here, we show that, as a direct consequence of this Runx3-deficiency, CD4+ TRM cells lacked the transforming growth factor (TGF)-β-responsive transcriptional network that underpins the tissue residency of epithelial CD8+ TRM cells. While CD4+ TRM cell formation required Runx1, this, along with the modest expression of Runx3 in CD4+ TRM cells, was insufficient to engage the TGF-β-driven residency program. Ectopic expression of Runx3 in CD4+ T cells incited this TGF-β-transcriptional network to promote prolonged survival, decreased tissue egress, a microanatomical redistribution towards epithelial layers and enhanced effector functionality. Thus, our results reveal distinct programming of tissue residency in CD8+ and CD4+ TRM cell subsets that is attributable to divergent Runx3 activity.
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Affiliation(s)
- Raíssa Fonseca
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Thomas N Burn
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Luke C Gandolfo
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia
- Walter and Eliza Hall Institute for Medical Research, Parkville, Victoria, Australia
| | - Sapna Devi
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Simone L Park
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Andreas Obers
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Maximilien Evrard
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Susan N Christo
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Frank A Buquicchio
- Department of Pathology, Stanford University, Stanford, CA, USA
- Program in Immunology, Stanford University, Stanford, CA, USA
| | - Caleb A Lareau
- Department of Pathology, Stanford University, Stanford, CA, USA
- Program in Immunology, Stanford University, Stanford, CA, USA
| | - Keely M McDonald
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Sarah K Sandford
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Natasha M Zamudio
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Nagela G Zanluqui
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Ali Zaid
- Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Southport, Queensland, Australia
| | - Terence P Speed
- School of Mathematics and Statistics, The University of Melbourne, Melbourne, Victoria, Australia
- Walter and Eliza Hall Institute for Medical Research, Parkville, Victoria, Australia
| | - Ansuman T Satpathy
- Department of Pathology, Stanford University, Stanford, CA, USA
- Program in Immunology, Stanford University, Stanford, CA, USA
- Parker Institute for Cancer Immunotherapy, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Scott N Mueller
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Francis R Carbone
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Laura K Mackay
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia.
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13
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Salim A, Molania R, Wang J, De Livera A, Thijssen R, Speed TP. RUV-III-NB: normalization of single cell RNA-seq data. Nucleic Acids Res 2022; 50:e96. [PMID: 35758618 PMCID: PMC9458465 DOI: 10.1093/nar/gkac486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 05/18/2022] [Accepted: 05/27/2022] [Indexed: 12/01/2022] Open
Abstract
Normalization of single cell RNA-seq data remains a challenging task. The performance of different methods can vary greatly between datasets when unwanted factors and biology are associated. Most normalization methods also only remove the effects of unwanted variation for the cell embedding but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. We propose RUV-III-NB, a method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. Using pseudo-replicates, RUV-III-NB explicitly takes into account potential association with biology when removing unwanted variation. The method can be used for both UMI or read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using published datasets with different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve DE analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent and is not sensitive to the number of factors assumed to contribute to the unwanted variation.
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Affiliation(s)
- Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, VIC 3053, Australia.,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,School of Mathematics and Statistics, University of Melbourne, VIC 3010, Australia.,Baker Heart and Diabetes Institute Melbourne, VIC 3004, Australia.,Department of Mathematics and Statistics, La Trobe University, VIC 3086, Australia
| | - Ramyar Molania
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia
| | - Jianan Wang
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,Department of Medical Biology, University of Melbourne, VIC 3010, Australia
| | - Alysha De Livera
- Melbourne School of Population and Global Health, University of Melbourne, VIC 3053, Australia.,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,Baker Heart and Diabetes Institute Melbourne, VIC 3004, Australia.,Department of Mathematics and Statistics, La Trobe University, VIC 3086, Australia.,School of Science, RMIT University, Melbourne VIC 3000, Australia
| | - Rachel Thijssen
- Blood Cells and Blood Cancer Division, Walter and Eliza Hall Institute of Medical Research, Parkville VIC 3052, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research Parkville, VIC 3052, Australia.,School of Mathematics and Statistics, University of Melbourne, VIC 3010, Australia
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14
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Speed TP, Hicks DG. Spectral PCA for MANOVA and data over binary trees. J MULTIVARIATE ANAL 2022. [DOI: 10.1016/j.jmva.2021.104905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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15
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Modzelewski AJ, Shao W, Chen J, Lee A, Qi X, Noon M, Tjokro K, Sales G, Biton A, Anand A, Speed TP, Xuan Z, Wang T, Risso D, He L. A mouse-specific retrotransposon drives a conserved Cdk2ap1 isoform essential for development. Cell 2021; 184:5541-5558.e22. [PMID: 34644528 PMCID: PMC8787082 DOI: 10.1016/j.cell.2021.09.021] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/26/2021] [Accepted: 09/14/2021] [Indexed: 12/13/2022]
Abstract
Retrotransposons mediate gene regulation in important developmental and pathological processes. Here, we characterized the transient retrotransposon induction during preimplantation development of eight mammals. Induced retrotransposons exhibit similar preimplantation profiles across species, conferring gene regulatory activities, particularly through long terminal repeat (LTR) retrotransposon promoters. A mouse-specific MT2B2 retrotransposon promoter generates an N-terminally truncated Cdk2ap1ΔN that peaks in preimplantation embryos and promotes proliferation. In contrast, the canonical Cdk2ap1 peaks in mid-gestation and represses cell proliferation. This MT2B2 promoter, whose deletion abolishes Cdk2ap1ΔN production, reduces cell proliferation and impairs embryo implantation, is developmentally essential. Intriguingly, Cdk2ap1ΔN is evolutionarily conserved in sequence and function yet is driven by different promoters across mammals. The distinct preimplantation Cdk2ap1ΔN expression in each mammalian species correlates with the duration of its preimplantation development. Hence, species-specific transposon promoters can yield evolutionarily conserved, alternative protein isoforms, bestowing them with new functions and species-specific expression to govern essential biological divergence.
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Affiliation(s)
- Andrew J Modzelewski
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Wanqing Shao
- Department of Genetics, Edison Family Center for Genome Science and System Biology, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jingqi Chen
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Angus Lee
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Xin Qi
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Mackenzie Noon
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Kristy Tjokro
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Gabriele Sales
- Department of Biology, University of Padova, Padova 35122, Italy
| | - Anne Biton
- Department of Statistics, University of California, Berkeley, Berkeley, CA 94720, USA; Bioinformatics and Biostatistics, Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris 75015, France
| | - Aparna Anand
- Department of Genetics, Edison Family Center for Genome Science and System Biology, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Terence P Speed
- Bioinformatics Division, WEHI, Parkville, VIC 3052, Australia
| | - Zhenyu Xuan
- Department of Biological Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
| | - Ting Wang
- Department of Genetics, Edison Family Center for Genome Science and System Biology, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Padova 35122, Italy.
| | - Lin He
- Division of Cellular and Developmental Biology, MCB Department, University of California, Berkeley, Berkeley, CA 94720, USA.
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16
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Foroutan M, Molania R, Pfefferle A, Behrenbruch C, Scheer S, Kallies A, Speed TP, Cursons J, Huntington ND. The Ratio of Exhausted to Resident Infiltrating Lymphocytes Is Prognostic for Colorectal Cancer Patient Outcome. Cancer Immunol Res 2021; 9:1125-1140. [PMID: 34413087 DOI: 10.1158/2326-6066.cir-21-0137] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/14/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
Immunotherapy success in colorectal cancer is mainly limited to patients whose tumors exhibit high microsatellite instability (MSI). However, there is variability in treatment outcomes within this group, which is in part driven by the frequency and characteristics of tumor-infiltrating immune cells. Indeed, the presence of specific infiltrating immune-cell subsets has been shown to correlate with immunotherapy response and is in many cases prognostic of treatment outcome. Tumor-infiltrating lymphocytes (TIL) can undergo distinct differentiation programs, acquiring features of tissue-residency or exhaustion, a process during which T cells upregulate inhibitory receptors, such as PD-1, and lose functionality. Although residency and exhaustion programs of CD8+ T cells are relatively well studied, these programs have only recently been appreciated in CD4+ T cells and remain largely unknown in tumor-infiltrating natural killer (NK) cells. In this study, we used single-cell RNA sequencing (RNA-seq) data to identify signatures of residency and exhaustion in colorectal cancer-infiltrating lymphocytes, including CD8+, CD4+, and NK cells. We then tested these signatures in independent single-cell data from tumor and normal tissue-infiltrating immune cells. Furthermore, we used versions of these signatures designed for bulk RNA-seq data to explore tumor-intrinsic mutations associated with residency and exhaustion from TCGA data. Finally, using two independent transcriptomic datasets from patients with colon adenocarcinoma, we showed that combinations of these signatures, in particular combinations of NK-cell activity signatures, together with tumor-associated signatures, such as TGFβ signaling, were associated with distinct survival outcomes in patients with colon adenocarcinoma.
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Affiliation(s)
- Momeneh Foroutan
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia.
| | - Ramyar Molania
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
| | - Aline Pfefferle
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia.,oNKo-innate Pty Ltd., Moonee Ponds, Victoria, Australia
| | - Corina Behrenbruch
- University of Melbourne Centre for Cancer Research, Parkville, Victoria, Australia
| | - Sebastian Scheer
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia
| | - Axel Kallies
- Department of Microbiology and Immunology, The University of Melbourne, Parkville, Victoria, Australia
| | - Terence P Speed
- Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,School of Mathematics & Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Joseph Cursons
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia. .,oNKo-innate Pty Ltd., Moonee Ponds, Victoria, Australia
| | - Nicholas D Huntington
- Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Clayton, Victoria, Australia. .,oNKo-innate Pty Ltd., Moonee Ponds, Victoria, Australia
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17
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Quaglieri A, Flensburg C, Speed TP, Majewski IJ. Finding a suitable library size to call variants in RNA-Seq. BMC Bioinformatics 2020; 21:553. [PMID: 33261552 PMCID: PMC7708150 DOI: 10.1186/s12859-020-03860-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/03/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. Here we specifically address how overall library size influences the detection of somatic mutations in RNA-seq data in two acute myeloid leukaemia datasets. RESULTS : We simulated shallower sequencing depths by downsampling 45 acute myeloid leukaemia samples (100 bp PE) that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%, average loss of 7%). The sensitivity in recovering insertions and deletions varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort (50 bp PE) and assessed the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 6% average loss in sensitivity using 40M fragments. CONCLUSIONS Between 30M and 40M 100 bp PE reads are needed to recover 90-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.
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Affiliation(s)
- Anna Quaglieri
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia. .,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Grattan St, Melbourne, 3010, Australia.
| | - Christoffer Flensburg
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia
| | - Terence P Speed
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia.,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Grattan St, Melbourne, 3010, Australia.,Department of Mathematics and Statistics, The University of Melbourne, 813 Swanston Street, Melbourne, 3010, Australia
| | - Ian J Majewski
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, 3052, Australia. .,Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Grattan St, Melbourne, 3010, Australia.
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18
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Morokoff A, Jones J, Nguyen H, Ma C, Lasocki A, Gaillard F, Bennett I, Luwor R, Stylli S, Paradiso L, Koldej R, Paldor I, Molania R, Speed TP, Webb A, Infusini G, Li J, Malpas C, Kalincik T, Drummond K, Siegal T, Kaye AH. Correction to: Serum microRNA is a biomarker for post-operative monitoring in glioma. J Neurooncol 2020; 149:401. [PMID: 33026635 DOI: 10.1007/s11060-020-03630-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
For the reference citation '[57]' in the second paragraph of the Results section of the original article there was no corresponding entry in the References section. It should have referred to the below mentioned article by Ebrahimkhani et al. (2018).
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Affiliation(s)
- Andrew Morokoff
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia. .,Department of Neurosurgery, Royal Melbourne Hospital, Parkville, VIC, Australia. .,Department of Surgery, University of Melbourne, Royal Melbourne Hospital Parkville, VIC, 3050, Australia.
| | - Jordan Jones
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia.,Department of Neurosurgery, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Hong Nguyen
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia
| | - Chenkai Ma
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia
| | - Arian Lasocki
- Department of Radiology, University of Melbourne, Parkville, VIC, Australia
| | - Frank Gaillard
- Department of Radiology, University of Melbourne, Parkville, VIC, Australia
| | - Iwan Bennett
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia
| | - Rod Luwor
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia
| | - Stanley Stylli
- Department of Neurosurgery, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Lucia Paradiso
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia
| | - Rachel Koldej
- Department of Medicine, University of Melbourne, Parkville, VIC, Australia
| | - Iddo Paldor
- Department of Neurosurgery, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Ramyar Molania
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
| | | | - Andrew Webb
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
| | | | - Jason Li
- Peter MacCallum Cancer Centre, Parkville, VIC, Australia
| | - Charles Malpas
- Clinical Outcomes Research Unit, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Tomas Kalincik
- Clinical Outcomes Research Unit, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Katharine Drummond
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia.,Department of Neurosurgery, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Tali Siegal
- Neuro-Oncology, Davidoff Institute of Oncology, Rabin Medical Center, Petach Tikva, Israel
| | - Andrew H Kaye
- Department of Surgery, University of Melbourne, Parkville, VIC, Australia.,Department of Neurosurgery, Hadassah Hebrew University, Jerusalem, Israel
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19
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Trussart M, Teh CE, Tan T, Leong L, Gray DH, Speed TP. Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets. eLife 2020; 9:59630. [PMID: 32894218 PMCID: PMC7500954 DOI: 10.7554/elife.59630] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 09/05/2020] [Indexed: 11/13/2022] Open
Abstract
Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes.
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Affiliation(s)
- Marie Trussart
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Charis E Teh
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Tania Tan
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Lawrence Leong
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Daniel Hd Gray
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
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20
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Poulos RC, Hains PG, Shah R, Lucas N, Xavier D, Manda SS, Anees A, Koh JMS, Mahboob S, Wittman M, Williams SG, Sykes EK, Hecker M, Dausmann M, Wouters MA, Ashman K, Yang J, Wild PJ, deFazio A, Balleine RL, Tully B, Aebersold R, Speed TP, Liu Y, Reddel RR, Robinson PJ, Zhong Q. Strategies to enable large-scale proteomics for reproducible research. Nat Commun 2020; 11:3793. [PMID: 32732981 PMCID: PMC7393074 DOI: 10.1038/s41467-020-17641-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 07/08/2020] [Indexed: 01/12/2023] Open
Abstract
Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics. Clinical proteomics critically depends on the ability to acquire highly reproducible data over an extended period of time. Here, the authors assess reproducibility over four months across different mass spectrometers and develop a computational approach to mitigate variation among instruments over time.
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Affiliation(s)
- Rebecca C Poulos
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Peter G Hains
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Rohan Shah
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Natasha Lucas
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Dylan Xavier
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Srikanth S Manda
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Asim Anees
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Jennifer M S Koh
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Sadia Mahboob
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Max Wittman
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Steven G Williams
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Erin K Sykes
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Michael Hecker
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Michael Dausmann
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Merridee A Wouters
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | | | - Jean Yang
- School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
| | - Peter J Wild
- Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany.,Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
| | - Anna deFazio
- Centre for Cancer Research, Westmead Institute for Medical Research, Westmead, NSW, Australia.,Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.,Department of Gynaecological Oncology, Westmead Hospital, Westmead, NSW, Australia
| | - Rosemary L Balleine
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Brett Tully
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.,Faculty of Science, University of Zürich, Zürich, Switzerland
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.,Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Roger R Reddel
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
| | - Qing Zhong
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia.
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21
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Jenkins NL, James SA, Salim A, Sumardy F, Speed TP, Conrad M, Richardson DR, Bush AI, McColl G. Changes in ferrous iron and glutathione promote ferroptosis and frailty in aging Caenorhabditis elegans. eLife 2020; 9:e56580. [PMID: 32690135 PMCID: PMC7373428 DOI: 10.7554/elife.56580] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 07/06/2020] [Indexed: 12/16/2022] Open
Abstract
All eukaryotes require iron. Replication, detoxification, and a cancer-protective form of regulated cell death termed ferroptosis, all depend on iron metabolism. Ferrous iron accumulates over adult lifetime in Caenorhabditis elegans. Here, we show that glutathione depletion is coupled to ferrous iron elevation in these animals, and that both occur in late life to prime cells for ferroptosis. We demonstrate that blocking ferroptosis, either by inhibition of lipid peroxidation or by limiting iron retention, mitigates age-related cell death and markedly increases lifespan and healthspan. Temporal scaling of lifespan is not evident when ferroptosis is inhibited, consistent with this cell death process acting at specific life phases to induce organismal frailty, rather than contributing to a constant aging rate. Because excess age-related iron elevation in somatic tissue, particularly in brain, is thought to contribute to degenerative disease, post-developmental interventions to limit ferroptosis may promote healthy aging.
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Affiliation(s)
- Nicole L Jenkins
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health and University of MelbourneParkvilleAustralia
| | | | - Agus Salim
- Department of Mathematics and Statistics, La Trobe UniversityBundooraAustralia
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical ResearchParkvilleAustralia
- Melbourne School of Population and Global Health, and School of Mathematics and Statistics, University of MelbourneMelbourneAustralia
| | - Fransisca Sumardy
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health and University of MelbourneParkvilleAustralia
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical ResearchParkvilleAustralia
- Department of Mathematics and Statistics, University of MelbourneMelbourneAustralia
| | - Marcus Conrad
- Helmholtz Zentrum München, Institute of Metabolism and Cell DeathNeuherbergGermany
| | - Des R Richardson
- Department of Pathology and Bosch Institute, University of SydneySydneyAustralia
| | - Ashley I Bush
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health and University of MelbourneParkvilleAustralia
| | - Gawain McColl
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health and University of MelbourneParkvilleAustralia
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22
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Ye C, Speed TP, Salim A. DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data. Bioinformatics 2020; 35:5155-5162. [PMID: 31197307 PMCID: PMC6954660 DOI: 10.1093/bioinformatics/btz453] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 05/08/2019] [Accepted: 06/06/2019] [Indexed: 12/20/2022] Open
Abstract
Motivation Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. Results We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. Availability and implementation The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chengzhong Ye
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.,School of Medicine, Tsinghua University, Haidian District, Beijing, China
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC, Australia
| | - Agus Salim
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC, Australia.,Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
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23
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Gresle MM, Jordan MA, Stankovich J, Spelman T, Johnson LJ, Laverick L, Hamlett A, Smith LD, Jokubaitis VG, Baker J, Haartsen J, Taylor B, Charlesworth J, Bahlo M, Speed TP, Brown MA, Field J, Baxter AG, Butzkueven H. Multiple sclerosis risk variants regulate gene expression in innate and adaptive immune cells. Life Sci Alliance 2020; 3:3/7/e202000650. [PMID: 32518073 PMCID: PMC7283543 DOI: 10.26508/lsa.202000650] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 05/27/2020] [Accepted: 05/28/2020] [Indexed: 12/20/2022] Open
Abstract
At least 200 single-nucleotide polymorphisms (SNPs) are associated with multiple sclerosis (MS) risk. A key function that could mediate SNP-encoded MS risk is their regulatory effects on gene expression. We performed microarrays using RNA extracted from purified immune cell types from 73 untreated MS cases and 97 healthy controls and then performed Cis expression quantitative trait loci mapping studies using additive linear models. We describe MS risk expression quantitative trait loci associations for 129 distinct genes. By extending these models to include an interaction term between genotype and phenotype, we identify MS risk SNPs with opposing effects on gene expression in cases compared with controls, namely, rs2256814 MYT1 in CD4 cells (q = 0.05) and rs12087340 RF00136 in monocyte cells (q = 0.04). The rs703842 SNP was also associated with a differential effect size on the expression of the METTL21B gene in CD8 cells of MS cases relative to controls (q = 0.03). Our study provides a detailed map of MS risk loci that function by regulating gene expression in cell types relevant to MS.
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Affiliation(s)
- Melissa M Gresle
- Department of Medicine, University of Melbourne, Parkville, Australia.,Melbourne Brain Centre, Royal Melbourne Hospital, University of Melbourne, Parkville, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Margaret A Jordan
- Molecular & Cell Biology, James Cook University, Townsville, Australia
| | - Jim Stankovich
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Tim Spelman
- Department of Medicine, University of Melbourne, Parkville, Australia.,Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Laura J Johnson
- Florey Institutes of Neuroscience and Mental Health, Parkville, Australia
| | - Louise Laverick
- Department of Medicine, University of Melbourne, Parkville, Australia
| | - Alison Hamlett
- Florey Institutes of Neuroscience and Mental Health, Parkville, Australia
| | - Letitia D Smith
- Molecular & Cell Biology, James Cook University, Townsville, Australia
| | - Vilija G Jokubaitis
- Department of Medicine, University of Melbourne, Parkville, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Josephine Baker
- Multiple Sclerosis Clinical & Research Unit, Melbourne Health, Royal Melbourne Hospital, Parkville, Australia
| | - Jodi Haartsen
- Eastern Clinical Research Unit, Eastern Health, Box Hill, Australia
| | - Bruce Taylor
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Jac Charlesworth
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Melanie Bahlo
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Matthew A Brown
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Translational Research Institute, Woolloongabba, Australia.,Guy's and St Thomas' NHS Foundation Trust and King's College London NIHR Biomedical Research Centre, London, England
| | - Judith Field
- Florey Institutes of Neuroscience and Mental Health, Parkville, Australia
| | - Alan G Baxter
- Molecular & Cell Biology, James Cook University, Townsville, Australia
| | - Helmut Butzkueven
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
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Lin Y, Cao Y, Kim HJ, Salim A, Speed TP, Lin DM, Yang P, Yang JYH. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Mol Syst Biol 2020; 16:e9389. [PMID: 32567229 PMCID: PMC7306901 DOI: 10.15252/msb.20199389] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 05/22/2020] [Accepted: 05/26/2020] [Indexed: 12/26/2022] Open
Abstract
Automated cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalise on the large collection of well-annotated scRNA-seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single-cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state-of-the-art methodology in automated cell type identification from scRNA-seq data.
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Affiliation(s)
- Yingxin Lin
- School of Mathematics and StatisticsUniversity of SydneySydneyNSWAustralia
- Charles Perkins CentreUniversity of SydneySydneyNSWAustralia
| | - Yue Cao
- School of Mathematics and StatisticsUniversity of SydneySydneyNSWAustralia
- Charles Perkins CentreUniversity of SydneySydneyNSWAustralia
| | - Hani Jieun Kim
- School of Mathematics and StatisticsUniversity of SydneySydneyNSWAustralia
- Charles Perkins CentreUniversity of SydneySydneyNSWAustralia
- Computational Systems Biology GroupChildren's Medical Research InstituteUniversity of SydneyWestmeadNSWAustralia
| | - Agus Salim
- Department of Mathematics and StatisticsLa Trobe UniversityBundooraVICAustralia
- Baker Heart and Diabetes InstituteMelbourneVICAustralia
- Bioinformatics DivisionWalter and Eliza Hall Institute of Medical ResearchParkvilleVICAustralia
| | - Terence P Speed
- Bioinformatics DivisionWalter and Eliza Hall Institute of Medical ResearchParkvilleVICAustralia
| | - David M Lin
- Department of Biomedical SciencesCornell UniversityIthacaNYUSA
| | - Pengyi Yang
- School of Mathematics and StatisticsUniversity of SydneySydneyNSWAustralia
- Charles Perkins CentreUniversity of SydneySydneyNSWAustralia
- Computational Systems Biology GroupChildren's Medical Research InstituteUniversity of SydneyWestmeadNSWAustralia
| | - Jean Yee Hwa Yang
- School of Mathematics and StatisticsUniversity of SydneySydneyNSWAustralia
- Charles Perkins CentreUniversity of SydneySydneyNSWAustralia
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25
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Molania R, Gagnon-Bartsch JA, Dobrovic A, Speed TP. A new normalization for Nanostring nCounter gene expression data. Nucleic Acids Res 2020; 47:6073-6083. [PMID: 31114909 PMCID: PMC6614807 DOI: 10.1093/nar/gkz433] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2018] [Revised: 04/25/2019] [Accepted: 05/07/2019] [Indexed: 12/18/2022] Open
Abstract
The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology.
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Affiliation(s)
- Ramyar Molania
- Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria 3084, Australia.,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia.,Department of Medicine, The University of Melbourne, Austin Health, Heidelberg, Victoria 3084, Australia
| | | | - Alexander Dobrovic
- Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria 3084, Australia.,School of Cancer Medicine and Molecular Cancer Prevention Program, La Trobe University, Bundoora, Victoria 3086, Australia.,Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010, Australia.,Department of Surgery, The University of Melbourne, Austin Health, Heidelberg, Victoria, 3084, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia.,Department of Mathematics and Statistics, University of Melbourne, Victoria 3010, Australia
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26
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Haupt S, Caramia F, Herschtal A, Soussi T, Lozano G, Chen H, Liang H, Speed TP, Haupt Y. Identification of cancer sex-disparity in the functional integrity of p53 and its X chromosome network. Nat Commun 2019; 10:5385. [PMID: 31772231 PMCID: PMC6879765 DOI: 10.1038/s41467-019-13266-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 10/31/2019] [Indexed: 12/12/2022] Open
Abstract
The disproportionately high prevalence of male cancer is poorly understood. We tested for sex-disparity in the functional integrity of the major tumor suppressor p53 in sporadic cancers. Our bioinformatics analyses expose three novel levels of p53 impact on sex-disparity in 12 non-reproductive cancer types. First, TP53 mutation is more frequent in these cancers among US males than females, with poorest survival correlating with its mutation. Second, numerous X-linked genes are associated with p53, including vital genomic regulators. Males are at unique risk from alterations of their single copies of these genes. High expression of X-linked negative regulators of p53 in wild-type TP53 cancers corresponds with reduced survival. Third, females exhibit an exceptional incidence of non-expressed mutations among p53-associated X-linked genes. Our data indicate that poor survival in males is contributed by high frequencies of TP53 mutations and an inability to shield against deregulated X-linked genes that engage in p53 networks.
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Affiliation(s)
- Sue Haupt
- Tumor Suppression Laboratory, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, Victoria, 3000, Australia. .,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, 3010, Australia.
| | - Franco Caramia
- Tumor Suppression Laboratory, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Alan Herschtal
- Department of Biometrics Novotech, Carlton, Victoria, 3053, Australia
| | - Thierry Soussi
- Department of Oncology-Pathology, Karolinska Institute, Cancer Center Karolinska, Solna, Sweden.,INSERM, U1138, Centre de Recherche des Cordeliers, Paris, France
| | - Guillermina Lozano
- The University of Texas, MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Hu Chen
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Han Liang
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.,Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, 3052, Australia.,Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Ygal Haupt
- Tumor Suppression Laboratory, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, Victoria, 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, 3010, Australia.,Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, 3010, Australia.,Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia
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27
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Gigante S, Gouil Q, Lucattini A, Keniry A, Beck T, Tinning M, Gordon L, Woodruff C, Speed TP, Blewitt ME, Ritchie ME. Using long-read sequencing to detect imprinted DNA methylation. Nucleic Acids Res 2019; 47:e46. [PMID: 30793194 PMCID: PMC6486641 DOI: 10.1093/nar/gkz107] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 01/14/2019] [Accepted: 02/08/2019] [Indexed: 02/01/2023] Open
Abstract
Systematic variation in the methylation of cytosines at CpG sites plays a critical role in early development of humans and other mammals. Of particular interest are regions of differential methylation between parental alleles, as these often dictate monoallelic gene expression, resulting in parent of origin specific control of the embryonic transcriptome and subsequent development, in a phenomenon known as genomic imprinting. Using long-read nanopore sequencing we show that, with an average genomic coverage of ∼10, it is possible to determine both the level of methylation of CpG sites and the haplotype from which each read arises. The long-read property is exploited to characterize, using novel methods, both methylation and haplotype for reads that have reduced basecalling precision compared to Sanger sequencing. We validate the analysis both through comparison of nanopore-derived methylation patterns with those from Reduced Representation Bisulfite Sequencing data and through comparison with previously reported data. Our analysis successfully identifies known imprinting control regions (ICRs) as well as some novel differentially methylated regions which, due to their proximity to hitherto unknown monoallelically expressed genes, may represent new ICRs.
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Affiliation(s)
- Scott Gigante
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia.,Department of Genetics, Yale University, 333 Cedar Street, New Haven CT 06510, USA
| | - Quentin Gouil
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia.,Department of Medical Biology, The University of Melbourne, Melbourne VIC 3010, Australia
| | - Alexis Lucattini
- Australian Genome Research Facility, 305 Grattan Street, Melbourne VIC 3000, Australia
| | - Andrew Keniry
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia.,Department of Medical Biology, The University of Melbourne, Melbourne VIC 3010, Australia
| | - Tamara Beck
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia
| | - Matthew Tinning
- Australian Genome Research Facility, 305 Grattan Street, Melbourne VIC 3000, Australia
| | - Lavinia Gordon
- Australian Genome Research Facility, 305 Grattan Street, Melbourne VIC 3000, Australia
| | - Chris Woodruff
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia
| | - Terence P Speed
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne VIC 3010, Australia
| | - Marnie E Blewitt
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia.,Department of Medical Biology, The University of Melbourne, Melbourne VIC 3010, Australia
| | - Matthew E Ritchie
- The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville VIC 3052, Australia.,Department of Medical Biology, The University of Melbourne, Melbourne VIC 3010, Australia.,School of Mathematics and Statistics, The University of Melbourne, Melbourne VIC 3010, Australia
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28
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Peters TJ, French HJ, Bradford ST, Pidsley R, Stirzaker C, Varinli H, Nair S, Qu W, Song J, Giles KA, Statham AL, Speirs H, Speed TP, Clark SJ. Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements. Bioinformatics 2019; 35:560-570. [PMID: 30084929 PMCID: PMC6378945 DOI: 10.1093/bioinformatics/bty675] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 07/10/2018] [Accepted: 07/31/2018] [Indexed: 01/23/2023] Open
Abstract
Motivation A synoptic view of the human genome benefits chiefly from the application of nucleic acid sequencing and microarray technologies. These platforms allow interrogation of patterns such as gene expression and DNA methylation at the vast majority of canonical loci, allowing granular insights and opportunities for validation of original findings. However, problems arise when validating against a “gold standard” measurement, since this immediately biases all subsequent measurements towards that particular technology or protocol. Since all genomic measurements are estimates, in the absence of a ”gold standard” we instead empirically assess the measurement precision and sensitivity of a large suite of genomic technologies via a consensus modelling method called the row-linear model. This method is an application of the American Society for Testing and Materials Standard E691 for assessing interlaboratory precision and sources of variability across multiple testing sites. Both cross-platform and cross-locus comparisons can be made across all common loci, allowing identification of technology- and locus-specific tendencies. Results We assess technologies including the Infinium MethylationEPIC BeadChip, whole genome bisulfite sequencing (WGBS), two different RNA-Seq protocols (PolyA+ and Ribo-Zero) and five different gene expression array platforms. Each technology thus is characterised herein, relative to the consensus. We showcase a number of applications of the row-linear model, including correlation with known interfering traits. We demonstrate a clear effect of cross-hybridisation on the sensitivity of Infinium methylation arrays. Additionally, we perform a true interlaboratory test on a set of samples interrogated on the same platform across twenty-one separate testing laboratories. Availability and implementation A full implementation of the row-linear model, plus extra functions for visualisation, are found in the R package consensus at https://github.com/timpeters82/consensus. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Timothy J Peters
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Hugh J French
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, NSW, Australia
| | - Stephen T Bradford
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,CSIRO Health and Biosecurity, North Ryde, NSW, Australia
| | - Ruth Pidsley
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Clare Stirzaker
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW, Australia
| | - Hilal Varinli
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,CSIRO Health and Biosecurity, North Ryde, NSW, Australia.,Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia.,NSW Ministry of Health, LMB 961, North Sydney, NSW, Australia
| | - Shalima Nair
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Wenjia Qu
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Jenny Song
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Katherine A Giles
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Aaron L Statham
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Helen Speirs
- Ramaciotti Centre for Genomics, University of New South Wales, Randwick, NSW, Australia
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Mathematics & Statistics, University of Melbourne, Melbourne, VIC, Australia
| | - Susan J Clark
- Epigenetics Laboratory, Genomics and Epigenetics Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, UNSW, Darlinghurst, NSW, Australia
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29
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Kim ML, Martin WJ, Minigo G, Keeble JL, Garnham AL, Pacini G, Smyth GK, Speed TP, Carapetis J, Wicks IP. Dysregulated IL-1β-GM-CSF Axis in Acute Rheumatic Fever That Is Limited by Hydroxychloroquine. Circulation 2019; 138:2648-2661. [PMID: 30571257 DOI: 10.1161/circulationaha.118.033891] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Acute rheumatic fever (ARF) and rheumatic heart disease are autoimmune consequences of group A streptococcus infection and remain major causes of cardiovascular morbidity and mortality around the world. Improved treatment has been stymied by gaps in understanding key steps in the immunopathogenesis of ARF and rheumatic heart disease. This study aimed to identify (1) effector T cell cytokine(s) that might be dysregulated in the autoimmune response of patients with ARF by group A streptococcus, and (2) an immunomodulatory agent that suppresses this response and could be clinically translatable to high-risk patients with ARF. METHODS The immune response to group A streptococcus was analyzed in peripheral blood mononuclear cells from an Australian Aboriginal ARF cohort by a combination of multiplex cytokine array, flow cytometric analysis, and global gene expression analysis by RNA sequencing. The immunomodulatory drug hydroxychloroquine was tested for effects on this response. RESULTS We found a dysregulated interleukin-1β-granulocyte-macrophage colony-stimulating factor (GM-CSF) cytokine axis in ARF peripheral blood mononuclear cells exposed to group A streptococcus in vitro, whereby persistent interleukin-1β production is coupled to overproduction of GM-CSF and selective expansion of CXCR3+CCR4-CCR6- CD4 T cells. CXCR3+CCR4-CCR6- CD4 T cells are the major source of GM-CSF in human CD4 T cells and CXCL10, a CXCR3 ligand and potent T helper 1 chemoattractant, was elevated in sera from patients with ARF. GM-CSF has recently emerged as a key T cell-derived effector cytokine in numerous autoimmune diseases, including myocarditis, and the production of CXCL10 may explain selective trafficking of these cells to the heart. We provide evidence that interleukin-1β amplifies the expansion of GM-CSF-expressing CD4 T cells, which is effectively suppressed by hydroxychloroquine. RNA sequencing showed shifts in gene expression profiles and differentially expressed genes in peripheral blood mononuclear cells derived from patients at different clinical stages of ARF. CONCLUSIONS Given the safety profile of hydroxychloroquine and its clinical pedigree in treating autoimmune diseases such as rheumatoid arthritis, where GM-CSF plays a pivotal role, we propose that hydroxychloroquine could be repurposed to reduce the risk of rheumatic heart disease after ARF.
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Affiliation(s)
- Man Lyang Kim
- Divisions of Inflammation (M.L.K., W.J.M., J.L.K., I.P.W.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Medical Biology (M.L.K., W.J.M., J.L.K., A.L.G., I.P.W.), University of Melbourne, Parkville, Victoria, Australia
| | - William J Martin
- Divisions of Inflammation (M.L.K., W.J.M., J.L.K., I.P.W.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Medical Biology (M.L.K., W.J.M., J.L.K., A.L.G., I.P.W.), University of Melbourne, Parkville, Victoria, Australia
| | - Gabriela Minigo
- Menzies School of Health Research and Charles Darwin University, Casuarina, Northern Territory, Australia (G.M.)
| | - Joanne L Keeble
- Divisions of Inflammation (M.L.K., W.J.M., J.L.K., I.P.W.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Medical Biology (M.L.K., W.J.M., J.L.K., A.L.G., I.P.W.), University of Melbourne, Parkville, Victoria, Australia
| | - Alexandra L Garnham
- Bioinformatics (A.L.G., G.P., G.K.S., T.P.S.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Medical Biology (M.L.K., W.J.M., J.L.K., A.L.G., I.P.W.), University of Melbourne, Parkville, Victoria, Australia
| | - Guido Pacini
- Bioinformatics (A.L.G., G.P., G.K.S., T.P.S.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
| | - Gordon K Smyth
- Bioinformatics (A.L.G., G.P., G.K.S., T.P.S.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Departments of Mathematics and Statistics (G.K.S., T.P.S.), University of Melbourne, Parkville, Victoria, Australia
| | - Terence P Speed
- Bioinformatics (A.L.G., G.P., G.K.S., T.P.S.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Departments of Mathematics and Statistics (G.K.S., T.P.S.), University of Melbourne, Parkville, Victoria, Australia
| | - Jonathan Carapetis
- Telethon Kids Institute, University of Western Australia, Princess Margaret Hospital for Children, Perth, Western Australia (J.C.)
| | - Ian P Wicks
- Divisions of Inflammation (M.L.K., W.J.M., J.L.K., I.P.W.), Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.,Medical Biology (M.L.K., W.J.M., J.L.K., A.L.G., I.P.W.), University of Melbourne, Parkville, Victoria, Australia.,Rheumatology Unit, Royal Melbourne Hospital, Parkville, Victoria, Australia (I.P.W.)
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30
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Savas P, Virassamy B, Ye C, Salim A, Mintoff CP, Caramia F, Salgado R, Teo ZL, Dushyanthen S, Byrne A, Luen SJ, Fox SB, Speed TP, Mackay LK, Neeson PJ, Loi S. Abstract PD5-03: Characterization of high TIL breast cancers reveals a prognostic and functionally distinct tissue-resident memory subpopulation. Cancer Res 2019. [DOI: 10.1158/1538-7445.sabcs18-pd5-03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background: Tumor infiltrating lymphocytes (TILs) assessed via light microscopy are prognostic and predictive in the early stage and advanced triple negative and HER2-amplified breast cancer (BC). Higher TILs can also identify patients more likely to benefit from anti-PD-1 therapy. In this study we interrogated T cell subsets that comprise high TILs to determine if distinct subpopulations are key mediators of anti-tumor immunity.
Methods: We characterised TILs with a focus on CD3+ T cells in 129 primary and metastatic BC samples using flow cytometry, bulk RNASeq on flow sorted T cell populations, multiplex immunohistochemistry and microdroplet based single cell 3' mRNA sequencing on the 10X Genomics Chromium platform. Cell type specific gene expression signatures were determined from differential expression between putative T cell subpopulations. These signatures were investigated in clinical cohorts, including trial cohorts treated with pembrolizumab.
Results: High TIL Infiltrates consisted primarily of CD3+ T cells, with both CD8 and CD4 populations. Unsupervised clustering of single cell sequencing identified 9 CD8 and CD4 subpopulations with distinct gene expression profiles. In addition to Tregs and CD8 effector memory (TEM) T cells, we found a CD8+ tissue resident memory (TRM) population expressing greater levels of T-cell checkpoints and cytotoxic markers compared to effector memory cells. In 2 primary tumours and 1 liver metastasis, bulk RNASeq of flow sorted TEM and TRM corroborated the single cell mRNASeq results. T cell receptor profiling (TCR) in the 3 samples found non-overlapping repertoires in the 2 primary tumours, but overlap in one metastatic lesion, suggesting divergent developmental origins in the breast, but the potential for nascent TRM differentiation in a metastatic niche. Clustering of these TCRs suggested differing antigen specificities between TRM and non-TRM CD8 T cells. Using Metabric data, the CD8 TRM gene expression signature was prognostic for disease free survival (DFS) in primary TNBCs (n=329, log-rank p=0.003), and was able to further stratify cases with high and low CD8A expression for DFS (log-rank p = 0.03). The CD8 TRM signature was enriched in baseline tumour samples of responders (n = 9) compared with non-responders (n=36) in 45 patients with metastatic melanoma treated with T cell checkpoint blockade (p < 0.0001). Additional single cell sequencing data with TCR sequencing will be combined with these initial results, and an independent data set of single cell mRNASeq and TCR Seq on CD3+ BC TILs will be used to confirm our findings. Cell type specific signatures will be explored in additional clinical cohorts including KEYNOTE-086, and presented at the meeting.
Conclusion: Using single cell profiling of the immune microenvironment in BC we demonstrate that high TIL BCs contain multiple T cell subpopulations with different functional and prognostic significance. Our approach identified a CD8 TRM population with a distinct gene expression profile and strong expression of key immune checkpoints likely representing the presence of true tumor specific immunity. This population may be a key target of immune checkpoint blockade.
Citation Format: Savas P, Virassamy B, Ye C, Salim A, Mintoff CP, Caramia F, Salgado R, Teo ZL, Dushyanthen S, Byrne A, Luen SJ, Fox SB, Speed TP, Mackay LK, Neeson PJ, Loi S. Characterization of high TIL breast cancers reveals a prognostic and functionally distinct tissue-resident memory subpopulation [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD5-03.
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Affiliation(s)
- P Savas
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - B Virassamy
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - C Ye
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - A Salim
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - CP Mintoff
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - F Caramia
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - R Salgado
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - ZL Teo
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - S Dushyanthen
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - A Byrne
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - SJ Luen
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - SB Fox
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - TP Speed
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - LK Mackay
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - PJ Neeson
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
| | - S Loi
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; University of Melbourne, Parkville, Victoria, Australia; Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia; Tsinghua University, Beijing, Haidian Qu, China; La Trobe University, Melbourne, Victoria, Australia; Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; GZA Ziekenhuizen, Antwerp, Belgium
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31
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Abstract
New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.
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Affiliation(s)
- Damien G. Hicks
- Centre for Micro-Photonics, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
| | - Terence P. Speed
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
| | - Mohammed Yassin
- Peter MacCallum Cancer Centre, Parkville, Victoria 3052, Australia
| | - Sarah M. Russell
- Centre for Micro-Photonics, Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia
- Peter MacCallum Cancer Centre, Parkville, Victoria 3052, Australia
- Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria 3050, Australia
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32
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Colborn KL, Mueller I, Speed TP. Joint Modeling of Mixed Plasmodium Species Infections Using a Bivariate Poisson Lognormal Model. Am J Trop Med Hyg 2018; 98:71-76. [PMID: 29182143 DOI: 10.4269/ajtmh.17-0523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Infectious diseases often present as coinfections that may affect each other in positive or negative ways. Understanding the relationship between two coinfecting pathogens is thus important to understand the risk of infection and burden of disease caused by each pathogen. Although coinfections with Plasmodium falciparum and Plasmodium vivax are very common outside Africa, it is yet unclear whether infections by the two parasite species are positively associated or if infection by one parasite suppresses the other. In this study, we use bivariate Poisson lognormal models (BPLM) to estimate covariate-adjusted associations between the incidence of infections (as measured by the force of blood-stage infections, molFOI) and clinical episodes caused by both P. falciparum and P. vivax in a cohort of Papua New Guinean children. A BPLM permits estimation of either positive or negative correlation, unlike most other multivariate Poisson models. Our results demonstrated a moderately positive association between P. falciparum and P. vivax infection rates, arguing against the hypothesis that P. vivax infections protect against P. falciparum infections. Our findings also suggest that the BPLM is only useful for counts with suitably large means and overdispersion.
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Affiliation(s)
- Kathryn L Colborn
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado
| | - Ivo Mueller
- Walter and Eliza Hall Institute, Melbourne, Australia
| | - Terence P Speed
- Department of Statistics, University of California, Berkeley, Berkeley, California.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.,Walter and Eliza Hall Institute, Melbourne, Australia
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33
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Savas P, Virassamy B, Ye C, Salim A, Mintoff CP, Caramia F, Salgado R, Byrne DJ, Teo ZL, Dushyanthen S, Byrne A, Wein L, Luen SJ, Poliness C, Nightingale SS, Skandarajah AS, Gyorki DE, Thornton CM, Beavis PA, Fox SB, Darcy PK, Speed TP, Mackay LK, Neeson PJ, Loi S. Publisher Correction: Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 2018; 24:1941. [PMID: 30135555 DOI: 10.1038/s41591-018-0176-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In the version of this article originally published, the institution in affiliation 10 was missing. Affiliation 10 was originally listed as Department of Surgery, Royal Melbourne Hospital and Royal Womens' Hospital, Melbourne, Victoria, Australia. It should have been Department of Surgery, Royal Melbourne Hospital and Royal Womens' Hospital, University of Melbourne, Melbourne, Victoria, Australia. The error has been corrected in the HTML and PDF versions of this article.
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Affiliation(s)
- Peter Savas
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Balaji Virassamy
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Chengzhong Ye
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia.,School of Medicine, Tsinghua University, Beijing, China
| | - Agus Salim
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.,Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria, Australia
| | - Christopher P Mintoff
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Franco Caramia
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Roberto Salgado
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Department of Pathology, GZA Ziekenhuizen, Antwerp, Belgium
| | - David J Byrne
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Zhi L Teo
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Sathana Dushyanthen
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Ann Byrne
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Lironne Wein
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen J Luen
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Catherine Poliness
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Sophie S Nightingale
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Anita S Skandarajah
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Department of Surgery Royal Melbourne Hospital and Royal Womens' Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David E Gyorki
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Chantel M Thornton
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Paul A Beavis
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen B Fox
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | | | - Phillip K Darcy
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Laura K Mackay
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
| | - Paul J Neeson
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
| | - Sherene Loi
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
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34
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Jacob L, Speed TP. The healthy ageing gene expression signature for Alzheimer's disease diagnosis: a random sampling perspective. Genome Biol 2018; 19:97. [PMID: 30045771 PMCID: PMC6060554 DOI: 10.1186/s13059-018-1481-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 07/06/2018] [Indexed: 11/23/2022] Open
Abstract
In a recent publication, Sood et al. (Genome Biol 16:185, 2015) presented a set of 150 probe sets that could be used in the diagnosis of Alzheimer’s disease (AD) based on gene expression. We reproduce some of their experiments and show that their signature is indeed able to discriminate between AD and control patients using blood gene expression in two cohorts. We also show that its performance does not stand out compared to randomly sampled sets of 150 probe sets from the same array.
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Affiliation(s)
- Laurent Jacob
- Université de Lyon, Université Lyon 1, and CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.
| | - Terence P Speed
- Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, and Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
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35
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Savas P, Virassamy B, Ye C, Salim A, Mintoff CP, Caramia F, Salgado R, Byrne DJ, Teo ZL, Dushyanthen S, Byrne A, Wein L, Luen SJ, Poliness C, Nightingale SS, Skandarajah AS, Gyorki DE, Thornton CM, Beavis PA, Fox SB, Darcy PK, Speed TP, Mackay LK, Neeson PJ, Loi S. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 2018; 24:986-993. [PMID: 29942092 DOI: 10.1038/s41591-018-0078-7] [Citation(s) in RCA: 601] [Impact Index Per Article: 100.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 04/25/2018] [Indexed: 12/18/2022]
Abstract
The quantity of tumor-infiltrating lymphocytes (TILs) in breast cancer (BC) is a robust prognostic factor for improved patient survival, particularly in triple-negative and HER2-overexpressing BC subtypes1. Although T cells are the predominant TIL population2, the relationship between quantitative and qualitative differences in T cell subpopulations and patient prognosis remains unknown. We performed single-cell RNA sequencing (scRNA-seq) of 6,311 T cells isolated from human BCs and show that significant heterogeneity exists in the infiltrating T cell population. We demonstrate that BCs with a high number of TILs contained CD8+ T cells with features of tissue-resident memory T (TRM) cell differentiation and that these CD8+ TRM cells expressed high levels of immune checkpoint molecules and effector proteins. A CD8+ TRM gene signature developed from the scRNA-seq data was significantly associated with improved patient survival in early-stage triple-negative breast cancer (TNBC) and provided better prognostication than CD8 expression alone. Our data suggest that CD8+ TRM cells contribute to BC immunosurveillance and are the key targets of modulation by immune checkpoint inhibition. Further understanding of the development, maintenance and regulation of TRM cells will be crucial for successful immunotherapeutic development in BC.
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Affiliation(s)
- Peter Savas
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Balaji Virassamy
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Chengzhong Ye
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia.,School of Medicine, Tsinghua University, Beijing, China
| | - Agus Salim
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.,Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria, Australia
| | - Christopher P Mintoff
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Franco Caramia
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Roberto Salgado
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Department of Pathology, GZA Ziekenhuizen, Antwerp, Belgium
| | - David J Byrne
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Zhi L Teo
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Sathana Dushyanthen
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Ann Byrne
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Lironne Wein
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen J Luen
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Catherine Poliness
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Sophie S Nightingale
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Anita S Skandarajah
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Department of Surgery Royal Melbourne Hospital and Royal Womens' Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - David E Gyorki
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Chantel M Thornton
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Paul A Beavis
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen B Fox
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | | | - Phillip K Darcy
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Terence P Speed
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Laura K Mackay
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
| | - Paul J Neeson
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
| | - Sherene Loi
- Division of Research, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, Victoria, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.
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36
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Rubin AF, Gelman H, Lucas N, Bajjalieh SM, Papenfuss AT, Speed TP, Fowler DM. Correction to: A statistical framework for analyzing deep mutational scanning data. Genome Biol 2018; 19:17. [PMID: 29415752 PMCID: PMC5803959 DOI: 10.1186/s13059-018-1391-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
CORRECTION After publication of our article [1] it was brought to our attention that a line of code was missing from our program to combine the within-replicate variance and between-replicate variance. This led to an overestimation of the standard errors calculated using the Enrich2 random-effects model.
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Affiliation(s)
- Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Australia.,Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Australia.,Department of Genome Sciences, University of Washington, Seattle, USA
| | - Hannah Gelman
- Department of Genome Sciences, University of Washington, Seattle, USA.,Institute for Protein Design, University of Washington, Seattle, USA
| | - Nathan Lucas
- Department of Pathology, University of Washington, Seattle, USA
| | | | - Anthony T Papenfuss
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Australia.,Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, USA. .,Department of Bioengineering, University of Washington, Seattle, USA.
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37
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Li J, Fu C, Speed TP, Wang W, Symmans WF. Accurate RNA Sequencing From Formalin-Fixed Cancer Tissue To Represent High-Quality Transcriptome From Frozen Tissue. JCO Precis Oncol 2018; 2018. [PMID: 29862382 PMCID: PMC5976456 DOI: 10.1200/po.17.00091] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Purpose Accurate transcriptional sequencing (RNA-seq) from formalin-fixation and paraffin-embedding (FFPE) tumor samples presents an important challenge for translational research and diagnostic development. In addition, there are now several different protocols to prepare a sequencing library from total RNA. We evaluated the accuracy of RNA-seq data generated from FFPE samples in terms of expression profiling. Methods We designed a biospecimen study to directly compare gene expression results from different protocols to prepare libraries for RNA-seq from human breast cancer tissues, with randomization to fresh-frozen (FF) or FFPE conditions. The protocols were compared using multiple computational methods to assess alignment of reads to reference genome, and the uniformity and continuity of coverage; as well as the variance and correlation, of overall gene expression and patterns of measuring coding sequence, phenotypic patterns of gene expression, and measurements from representative multigene signatures. Results The principal determinant of variance in gene expression was use of exon capture probes, followed by the conditions of preservation (FF versus FFPE), and phenotypic differences between breast cancers. One protocol, with RNase H-based rRNA depletion, exhibited least variability of gene expression measurements, strongest correlation between FF and FFPE samples, and was generally representative of the transcriptome from standard FF RNA-seq protocols. Conclusion Method of RNA-seq library preparation from FFPE samples had marked effect on the accuracy of gene expression measurement compared to matched FF samples. Nevertheless, some protocols produced highly concordant expression data from FFPE RNA-seq data, compared to RNA-seq results from matched frozen samples.
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Affiliation(s)
- Jialu Li
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Chunxiao Fu
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Terence P Speed
- Department of Statistics, University of California, Berkeley, Berkeley, California, USA.,Bioinformatics Division, The Walter and Eliza Hall. Institute of Medical Research, Parkville, Victoria, Australia.,Department of Mathematics and Statistics, The University of Melbourne, Victoria, Australia
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - W Fraser Symmans
- Departments of Pathology and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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38
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Rubin AF, Gelman H, Lucas N, Bajjalieh SM, Papenfuss AT, Speed TP, Fowler DM. A statistical framework for analyzing deep mutational scanning data. Genome Biol 2017; 18:150. [PMID: 28784151 PMCID: PMC5547491 DOI: 10.1186/s13059-017-1272-5] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 07/06/2017] [Indexed: 11/10/2022] Open
Abstract
Deep mutational scanning is a widely used method for multiplex measurement of functional consequences of protein variants. We developed a new deep mutational scanning statistical model that generates error estimates for each measurement, capturing both sampling error and consistency between replicates. We apply our model to one novel and five published datasets comprising 243,732 variants and demonstrate its superiority in removing noisy variants and conducting hypothesis testing. Simulations show our model applies to scans based on cell growth or binding and handles common experimental errors. We implemented our model in Enrich2, software that can empower researchers analyzing deep mutational scanning data.
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Affiliation(s)
- Alan F Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, VIC, 3010, Australia.,Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.,Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Hannah Gelman
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.,Institute for Protein Design, University of Washington, Seattle, WA, 98195, USA
| | - Nathan Lucas
- Department of Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Sandra M Bajjalieh
- Department of Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Anthony T Papenfuss
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, VIC, 3010, Australia.,Bioinformatics and Cancer Genomics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, 3010, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. .,Department of Bioengineering, University of Washington, Seattle, WA, 98195, USA.
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Savas P, Teo ZL, Lefevre C, Flensburg C, Caramia F, Alsop K, Mansour M, Francis PA, Thorne HA, Silva MJ, Kanu N, Dietzen M, Rowan A, Kschischo M, Fox S, Bowtell DD, Dawson SJ, Speed TP, Swanton C, Loi S. Correction: The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE". PLoS Med 2017; 14:e1002302. [PMID: 28430777 PMCID: PMC5400239 DOI: 10.1371/journal.pmed.1002302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
[This corrects the article DOI: 10.1371/journal.pmed.1002204.].
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Choi YJ, Lin CP, Risso D, Chen S, Kim TA, Tan MH, Li JB, Wu Y, Chen C, Xuan Z, Macfarlan T, Peng W, Lloyd KCK, Kim SY, Speed TP, He L. Deficiency of microRNA miR-34a expands cell fate potential in pluripotent stem cells. Science 2017; 355:science.aag1927. [PMID: 28082412 DOI: 10.1126/science.aag1927] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 12/14/2016] [Indexed: 12/13/2022]
Abstract
Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) efficiently generate all embryonic cell lineages but rarely generate extraembryonic cell types. We found that microRNA miR-34a deficiency expands the developmental potential of mouse pluripotent stem cells, yielding both embryonic and extraembryonic lineages and strongly inducing MuERV-L (MERVL) endogenous retroviruses, similar to what is seen with features of totipotent two-cell blastomeres. miR-34a restricts the acquisition of expanded cell fate potential in pluripotent stem cells, and it represses MERVL expression through transcriptional regulation, at least in part by targeting the transcription factor Gata2. Our studies reveal a complex molecular network that defines and restricts pluripotent developmental potential in cultured ESCs and iPSCs.
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Affiliation(s)
- Yong Jin Choi
- Division of Cellular and Developmental Biology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94705, USA
| | - Chao-Po Lin
- Division of Cellular and Developmental Biology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94705, USA.
| | - Davide Risso
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA 94720, USA
| | - Sean Chen
- Division of Cellular and Developmental Biology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94705, USA
| | - Thomas Aquinas Kim
- Division of Cellular and Developmental Biology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94705, USA
| | - Meng How Tan
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Jin Billy Li
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Yalei Wu
- Thermo Fisher Scientific, 180 Oyster Point Boulevard, South San Francisco, CA 94080, USA
| | - Caifu Chen
- Integrated DNA Technologies, 200 Chesapeake Drive, Redwood City, CA 94063, USA
| | - Zhenyu Xuan
- Department of Molecular and Cell Biology, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Todd Macfarlan
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD 20892, USA
| | - Weiqun Peng
- Department of Physics, George Washington University, Washington, DC 20052, USA
| | - K C Kent Lloyd
- Mouse Biology Program, University of California, Davis, CA 95616, USA
| | - Sang Yong Kim
- Department of Pathology, New York University School of Medicine, New York, NY 10016, USA
| | - Terence P Speed
- Department of Statistics, University of California, Berkeley, CA 94720, USA.,Department of Mathematics and Statistics, University of Melbourne, Parkville, VIC 3010, Australia.,Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
| | - Lin He
- Division of Cellular and Developmental Biology, Department of Molecular and Cell Biology, University of California, Berkeley, CA 94705, USA.
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Savas P, Teo ZL, Lefevre C, Flensburg C, Caramia F, Alsop K, Mansour M, Francis PA, Thorne HA, Silva MJ, Kanu N, Dietzen M, Rowan A, Kschischo M, Fox S, Bowtell DD, Dawson SJ, Speed TP, Swanton C, Loi S. The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program "CASCADE". PLoS Med 2016; 13:e1002204. [PMID: 28027312 PMCID: PMC5189956 DOI: 10.1371/journal.pmed.1002204] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 11/17/2016] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Understanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease. METHODS AND FINDINGS Three patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing. Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution. Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed. CONCLUSIONS This study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.
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Affiliation(s)
- Peter Savas
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Zhi Ling Teo
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Christophe Lefevre
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, Australia
| | - Christoffer Flensburg
- The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, Australia
| | - Franco Caramia
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Kathryn Alsop
- Cancer Genomics Program, Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Mariam Mansour
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Prudence A. Francis
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Heather A. Thorne
- Cancer Genomics Program, Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- kConFab, Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Maria Joao Silva
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Nnennaya Kanu
- UCL Cancer Institute, CRUK Lung Cancer Centre of Excellence, London, United Kingdom
| | - Michelle Dietzen
- UCL Cancer Institute, CRUK Lung Cancer Centre of Excellence, London, United Kingdom
| | - Andrew Rowan
- The Francis Crick Institute, London, United Kingdom
| | - Maik Kschischo
- University of Applied Sciences Koblenz, RheinAhrCampus Remagen, Department of Mathematics and Technology, Remagen, Germany
| | - Stephen Fox
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - David D. Bowtell
- Cancer Genomics Program, Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, the University of Melbourne, Victoria, Australia
| | - Sarah-Jane Dawson
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, the University of Melbourne, Victoria, Australia
| | - Terence P. Speed
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
| | - Charles Swanton
- UCL Cancer Institute, CRUK Lung Cancer Centre of Excellence, London, United Kingdom
- The Francis Crick Institute, London, United Kingdom
| | - Sherene Loi
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, the University of Melbourne, Victoria, Australia
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Gerstner JR, Koberstein JN, Watson AJ, Zapero N, Risso D, Speed TP, Frank MG, Peixoto L. Removal of unwanted variation reveals novel patterns of gene expression linked to sleep homeostasis in murine cortex. BMC Genomics 2016; 17:727. [PMID: 27801296 PMCID: PMC5088519 DOI: 10.1186/s12864-016-3065-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Why we sleep is still one of the most perplexing mysteries in biology. Strong evidence indicates that sleep is necessary for normal brain function and that sleep need is a tightly regulated process. Surprisingly, molecular mechanisms that determine sleep need are incompletely described. Moreover, very little is known about transcriptional changes that specifically accompany the accumulation and discharge of sleep need. Several studies have characterized differential gene expression changes following sleep deprivation. Much less is known, however, about changes in gene expression during the compensatory response to sleep deprivation (i.e. recovery sleep). RESULTS In this study we present a comprehensive analysis of the effects of sleep deprivation and subsequent recovery sleep on gene expression in the mouse cortex. We used a non-traditional analytical method for normalization of genome-wide gene expression data, Removal of Unwanted Variation (RUV). RUV improves detection of differential gene expression following sleep deprivation. We also show that RUV normalization is crucial to the discovery of differentially expressed genes associated with recovery sleep. Our analysis indicates that the majority of transcripts upregulated by sleep deprivation require 6 h of recovery sleep to return to baseline levels, while the majority of downregulated transcripts return to baseline levels within 1-3 h. We also find that transcripts that change rapidly during recovery (i.e. within 3 h) do so on average with a time constant that is similar to the time constant for the discharge of sleep need. CONCLUSIONS We demonstrate that proper data normalization is essential to identify changes in gene expression that are specifically linked to sleep deprivation and recovery sleep. Our results provide the first evidence that recovery sleep is comprised of two waves of transcriptional regulation that occur at different times and affect functionally distinct classes of genes.
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Affiliation(s)
- Jason R Gerstner
- Washington State University, Elson S. Floyd College of Medicine, Spokane, WA, 99202, USA
| | - John N Koberstein
- Washington State University, Elson S. Floyd College of Medicine, Spokane, WA, 99202, USA
| | - Adam J Watson
- Department of Neuroscience, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nikolai Zapero
- Washington State University, Elson S. Floyd College of Medicine, Spokane, WA, 99202, USA
| | - Davide Risso
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, 94720, USA
| | - Terence P Speed
- Department of Statistics, University of California, Berkeley, CA, USA.,Department of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia.,Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Marcos G Frank
- Washington State University, Elson S. Floyd College of Medicine, Spokane, WA, 99202, USA.
| | - Lucia Peixoto
- Washington State University, Elson S. Floyd College of Medicine, Spokane, WA, 99202, USA.
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Zhang Y, Feng ZP, Naselli G, Bell F, Wettenhall J, Auyeung P, Ellis JA, Ponsonby AL, Speed TP, Chong MM, Harrison LC. Corrigendum to ‘MicroRNAs in CD4+ T cell subsets are markers of disease risk and T cell dysfunction in individuals at risk for type 1 diabetes’ [J. Autoimmun. 68C (2016) 52–61]. J Autoimmun 2016; 73:130. [DOI: 10.1016/j.jaut.2016.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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44
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Poplawski SG, Peixoto L, Porcari GS, Wimmer ME, McNally AG, Mizuno K, Giese KP, Chatterjee S, Koberstein JN, Risso D, Speed TP, Abel T. Contextual fear conditioning induces differential alternative splicing. Neurobiol Learn Mem 2016; 134 Pt B:221-35. [PMID: 27451143 DOI: 10.1016/j.nlm.2016.07.018] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Revised: 07/16/2016] [Accepted: 07/19/2016] [Indexed: 12/20/2022]
Abstract
The process of memory consolidation requires transcription and translation to form long-term memories. Significant effort has been dedicated to understanding changes in hippocampal gene expression after contextual fear conditioning. However, alternative splicing by differential transcript regulation during this time period has received less attention. Here, we use RNA-seq to determine exon-level changes in expression after contextual fear conditioning and retrieval. Our work reveals that a short variant of Homer1, Ania-3, is regulated by contextual fear conditioning. The ribosome biogenesis regulator Las1l, small nucleolar RNA Snord14e, and the RNA-binding protein Rbm3 also change specific transcript usage after fear conditioning. The changes in Ania-3 and Las1l are specific to either the new context or the context-shock association, while the changes in Rbm3 occur after context or shock only. Our analysis revealed novel transcript regulation of previously undetected changes after learning, revealing the importance of high throughput sequencing approaches in the study of gene expression changes after learning.
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Affiliation(s)
- Shane G Poplawski
- Pharmacology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Lucia Peixoto
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA; Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Giulia S Porcari
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mathieu E Wimmer
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna G McNally
- Pharmacology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Keiko Mizuno
- Centre for the Cellular Basis of Behaviour, King's College London, London, UK
| | - K Peter Giese
- Centre for the Cellular Basis of Behaviour, King's College London, London, UK
| | | | - John N Koberstein
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Davide Risso
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
| | - Terence P Speed
- Department of Statistics, University of California, Berkeley, CA, USA; Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia; Department of Mathematics and Statistics, The University of Melbourne, Victoria, Australia
| | - Ted Abel
- Pharmacology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA; Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
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Lin SJ, Gagnon-Bartsch JA, Tan IB, Earle S, Ruff L, Pettinger K, Ylstra B, van Grieken N, Rha SY, Chung HC, Lee JS, Cheong JH, Noh SH, Aoyama T, Miyagi Y, Tsuburaya A, Yoshikawa T, Ajani JA, Boussioutas A, Yeoh KG, Yong WP, So J, Lee J, Kang WK, Kim S, Kameda Y, Arai T, zur Hausen A, Speed TP, Grabsch HI, Tan P. Signatures of tumour immunity distinguish Asian and non-Asian gastric adenocarcinomas. Gut 2015; 64:1721-31. [PMID: 25385008 PMCID: PMC4680172 DOI: 10.1136/gutjnl-2014-308252] [Citation(s) in RCA: 164] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Accepted: 09/09/2014] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Differences in gastric cancer (GC) clinical outcomes between patients in Asian and non-Asian countries has been historically attributed to variability in clinical management. However, recent international Phase III trials suggest that even with standardised treatments, GC outcomes differ by geography. Here, we investigated gene expression differences between Asian and non-Asian GCs, and if these molecular differences might influence clinical outcome. DESIGN We compared gene expression profiles of 1016 GCs from six Asian and three non-Asian GC cohorts, using a two-stage meta-analysis design and a novel biostatistical method (RUV-4) to adjust for technical variation between cohorts. We further validated our findings by computerised immunohistochemical analysis on two independent tissue microarray (TMA) cohorts from Asian and non-Asian localities (n=665). RESULTS Gene signatures differentially expressed between Asians and non-Asian GCs were related to immune function and inflammation. Non-Asian GCs were significantly enriched in signatures related to T-cell biology, including CTLA-4 signalling. Similarly, in the TMA cohorts, non-Asian GCs showed significantly higher expression of T-cell markers (CD3, CD45R0, CD8) and lower expression of the immunosuppressive T-regulatory cell marker FOXP3 compared to Asian GCs (p<0.05). Inflammatory cell markers CD66b and CD68 also exhibited significant cohort differences (p<0.05). Exploratory analyses revealed a significant relationship between tumour immunity factors, geographic locality-specific prognosis, and postchemotherapy outcomes. CONCLUSIONS Analyses of >1600 GCs suggest that Asian and non-Asian GCs exhibit distinct tumour immunity signatures related to T-cell function. These differences may influence geographical differences in clinical outcome, and the design of future trials particularly in immuno-oncology.
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Affiliation(s)
- Suling J Lin
- Department of Cancer Therapeutics and Stratified Oncology, Genome Institute of Singapore, Singapore, Singapore
| | | | - Iain Beehuat Tan
- Department of Medical Oncology, National Cancer Centre, Singapore, Singapore
| | - Sophie Earle
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Louise Ruff
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Katherine Pettinger
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Bauke Ylstra
- Department of Pathology, Free University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Nicole van Grieken
- Department of Pathology, Free University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Sun Young Rha
- Department of Internal Medicine, Yonsei Cancer Center, Seoul, South Korea
| | - Hyun Cheol Chung
- Department of Internal Medicine, Yonsei Cancer Center, Seoul, South Korea
| | - Ju-Seog Lee
- Division of Cancer Medicine, Department of Systems Biology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Jae Ho Cheong
- Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Hoon Noh
- Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea
| | - Toru Aoyama
- Department of Gastrointestinal Surgery, Kanagawa Cancer Center Hospital, Yokohama, Japan
| | - Yohei Miyagi
- Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Akira Tsuburaya
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Takaki Yoshikawa
- Department of Gastrointestinal Surgery, Kanagawa Cancer Center Hospital, Yokohama, Japan
| | - Jaffer A Ajani
- Departments of Gastrointestinal Medical Oncology, MD Anderson Cancer Center, Houston, USA
| | - Alex Boussioutas
- Cancer Genomics and Biochemistry Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia,Department of Medicine Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia
| | - Khay Guan Yeoh
- Department of Medicine, National University Health System, Singapore, Singapore,Department of Gastroenterology and Hepatology, National University Health System, Singapore, Singapore
| | - Wei Peng Yong
- National University Cancer Institute, Singapore, Singapore
| | - Jimmy So
- Department of Surgery, National University Health System, Singapore, Singapore
| | - Jeeyun Lee
- Department of Medicine, Division of Haematology-Oncology, Samsung Medical Centre, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Won Ki Kang
- Department of Medicine, Division of Haematology-Oncology, Samsung Medical Centre, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sung Kim
- Department of Surgery, Gastric Cancer Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yoichi Kameda
- Department of Pathology, Kanagawa Cancer Center Hospital, Yokohama, Japan
| | - Tomio Arai
- Department of Pathology, Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan
| | - Axel zur Hausen
- Department of Pathology, Maastricht University Medical Center, Maastricht, The Netherlands,GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Terence P Speed
- Department of Statistics, University of California at Berkeley, Berkeley, California, USA,Bioinformatics Division, Walter and Eliza Hall Institute, Victoria, Australia,Department of Mathematics and Statistics, University of Melbourne, Australia
| | - Heike I Grabsch
- Section of Pathology and Tumour Biology, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK,Department of Pathology, Maastricht University Medical Center, Maastricht, The Netherlands,GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Patrick Tan
- Department of Cancer Therapeutics and Stratified Oncology, Genome Institute of Singapore, Singapore, Singapore,Cancer and Stem Cell Biology Program, Duke-NUS Graduate Medical School, Singapore, Singapore,Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore,Cellular and Molecular Research, National Cancer Centre, Singapore, Singapore
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Huang KT, Mikeska T, Li J, Takano EA, Millar EKA, Graham PH, Boyle SE, Campbell IG, Speed TP, Dobrovic A, Fox SB. Assessment of DNA methylation profiling and copy number variation as indications of clonal relationship in ipsilateral and contralateral breast cancers to distinguish recurrent breast cancer from a second primary tumour. BMC Cancer 2015; 15:669. [PMID: 26452468 PMCID: PMC4600279 DOI: 10.1186/s12885-015-1676-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 10/01/2015] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Patients with breast cancer have an increased risk of developing subsequent breast cancers. It is important to distinguish whether these tumours are de novo or recurrences of the primary tumour in order to guide the appropriate therapy. Our aim was to investigate the use of DNA methylation profiling and array comparative genomic hybridization (aCGH) to determine whether the second tumour is clonally related to the first tumour. METHODS Methylation-sensitive high-resolution melting was used to screen promoter methylation in a panel of 13 genes reported as methylated in breast cancer (RASSF1A, TWIST1, APC, WIF1, MGMT, MAL, CDH13, RARβ, BRCA1, CDH1, CDKN2A, TP73, and GSTP1) in 29 tumour pairs (16 ipsilateral and 13 contralateral). Using the methylation profile of these genes, we employed a Bayesian and an empirical statistical approach to estimate clonal relationship. Copy number alterations were analysed using aCGH on the same set of tumour pairs. RESULTS There is a higher probability of the second tumour being recurrent in ipsilateral tumours compared with contralateral tumours (38 % versus 8 %; p <0.05) based on the methylation profile. Using previously reported recurrence rates as Bayesian prior probabilities, we classified 69 % of ipsilateral and 15 % of contralateral tumours as recurrent. The inferred clonal relationship results of the tumour pairs were generally concordant between methylation profiling and aCGH. CONCLUSION Our results show that DNA methylation profiling as well as aCGH have potential as diagnostic tools in improving the clinical decisions to differentiate recurrences from a second de novo tumour.
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Affiliation(s)
- Katie T Huang
- Molecular Pathology Research and Development Laboratory, Department of Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia. .,Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
| | - Thomas Mikeska
- Molecular Pathology Research and Development Laboratory, Department of Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia. .,Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia. .,Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Studley Road, Heidelberg, VIC, 3084, Australia.
| | - Jason Li
- Bioinformatics, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia.
| | - Elena A Takano
- Molecular Pathology Research and Development Laboratory, Department of Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia.
| | - Ewan K A Millar
- South Eastern Area Laboratory Service (SEALS), St. George Hospital, Gary Street, Kogarah, NSW, 2217, Australia. .,The Kinghorn Cancer Centre & Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia. .,School of Medicine and Health Sciences, University of Western Sydney, Narellan Road, Campbelltown, NSW, 2560, Australia. .,Faculty of Medicine, University of NSW, High Street, Kensington, NSW, 2052, Australia.
| | - Peter H Graham
- The Kinghorn Cancer Centre & Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia. .,School of Medicine and Health Sciences, University of Western Sydney, Narellan Road, Campbelltown, NSW, 2560, Australia.
| | - Samantha E Boyle
- VBCRC Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia.
| | - Ian G Campbell
- Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia. .,VBCRC Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia.
| | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Australia.
| | - Alexander Dobrovic
- Molecular Pathology Research and Development Laboratory, Department of Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia. .,Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia. .,Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Studley Road, Heidelberg, VIC, 3084, Australia. .,School of Cancer Medicine, La Trobe University, Bundoora, VIC, 3084, Australia.
| | - Stephen B Fox
- Molecular Pathology Research and Development Laboratory, Department of Pathology, Peter MacCallum Cancer Centre, St. Andrew's Place, East Melbourne, VIC, 3002, Australia. .,Department of Pathology and Sir Peter MacCallum Department of Oncology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
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Freytag S, Gagnon-Bartsch J, Speed TP, Bahlo M. Systematic noise degrades gene co-expression signals but can be corrected. BMC Bioinformatics 2015; 16:309. [PMID: 26403471 PMCID: PMC4583191 DOI: 10.1186/s12859-015-0745-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 09/16/2015] [Indexed: 12/31/2022] Open
Abstract
Background In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tested for this application. To our knowledge, the performances of these approaches have never been systematically compared in the context of gene co-expression estimation. Results Using simulations we demonstrate that standard cleaning procedures, such as background correction and quantile normalization, fail to adequately remove systematic noise that affects gene co-expression and at times further degrade true gene co-expression. Instead we show that a global version of removal of unwanted variation (RUV), a data-driven approach, removes systematic noise but also allows the estimation of the true underlying gene-gene correlations. We compare the performance of all noise removal methods when applied to five large published datasets on gene expression in the human brain. RUV retrieves the highest gene co-expression values for sets of genes known to interact, but also provides the greatest consistency across all five datasets. We apply the method to prioritize epileptic encephalopathy candidate genes. Conclusions Our work raises serious concerns about the quality of many published gene co-expression analyses. RUV provides an efficient and flexible way to remove systematic noise from high-dimensional microarray datasets when the objective is gene co-expression analysis. The RUV method as applicable in the context of gene-gene correlation estimation is available as a BioconductoR-package: RUVcorr. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0745-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Saskia Freytag
- Bioinformatics Division, Walter + Eliza Hall Institute, 1G Royal Parade, Melbourne, 3050, Australia. .,Department of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia.
| | - Johann Gagnon-Bartsch
- Department of Statistics, University of California, 367 Evans Hall, Berkeley, 94720, USA.
| | - Terence P Speed
- Bioinformatics Division, Walter + Eliza Hall Institute, 1G Royal Parade, Melbourne, 3050, Australia. .,Department of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia. .,Department of Statistics, University of California, 367 Evans Hall, Berkeley, 94720, USA.
| | - Melanie Bahlo
- Bioinformatics Division, Walter + Eliza Hall Institute, 1G Royal Parade, Melbourne, 3050, Australia. .,Department of Mathematics and Statistics, University of Melbourne, Melbourne, 3010, Australia. .,Department of Medical Biology, University of Melbourne, Melbourne, 3010, Australia.
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Jacob L, Gagnon-Bartsch JA, Speed TP. Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed. Biostatistics 2015; 17:16-28. [PMID: 26286812 PMCID: PMC4679071 DOI: 10.1093/biostatistics/kxv026] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Accepted: 06/25/2015] [Indexed: 11/13/2022] Open
Abstract
When dealing with large scale gene expression studies, observations are commonly contaminated by sources of unwanted variation such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g. when the goal is to cluster the samples or to build a corrected version of the dataset--as opposed to the study of an observed factor of interest--taking unwanted variation into account can become a difficult task. The factors driving unwanted variation may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data. The proposed methods are then evaluated on synthetic data and three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state-of-the-art corrections. All proposed methods are implemented in the bioconductor package RUVnormalize.
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Affiliation(s)
- Laurent Jacob
- Laboratoire de Biométrie et Biologie Évolutive, Université de Lyon, Université Lyon 1, CNRS, UMR, 5558 Lyon, France
| | | | - Terence P Speed
- Department of Statistics, University of California, Berkeley, CA 974720, USA and Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne 3052, Australia
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49
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Peixoto L, Risso D, Poplawski SG, Wimmer ME, Speed TP, Wood MA, Abel T. How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets. Nucleic Acids Res 2015. [PMID: 26202970 PMCID: PMC4652761 DOI: 10.1093/nar/gkv736] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or ‘batch effects’ can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic when it is influenced simultaneously by a variety of biological factors in addition to the one of interest. Using examples from experimental neuroscience, we show that batch effects can dominate the signal of interest; and that the choice of normalization method affects the power and reproducibility of the results. While commonly used global normalization methods are not able to adequately normalize the data, more recently developed RNA-seq normalization can. We focus on one particular method, RUVSeq and show that it is able to increase power and biological insight of the results. Finally, we provide a tutorial outlining the implementation of RUVSeq normalization that is applicable to a broad range of studies as well as meta-analysis of publicly available data.
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Affiliation(s)
- Lucia Peixoto
- Department of Biology, University of Pennsylvania, Smilow Center for Translational Research, Room 10-170, Building 421, 3400 Civic Center Boulevard, Philadelphia, PA 19104-6168, USA
| | - Davide Risso
- Division of Biostatistics, School of Public Health, University of California, Berkeley, 344 Li Ka Shing Center, #3370, Berkeley, CA 94720-3370, USA
| | - Shane G Poplawski
- Department of Biology, University of Pennsylvania, Smilow Center for Translational Research, Room 10-170, Building 421, 3400 Civic Center Boulevard, Philadelphia, PA 19104-6168, USA
| | - Mathieu E Wimmer
- Department of Biology, University of Pennsylvania, Smilow Center for Translational Research, Room 10-170, Building 421, 3400 Civic Center Boulevard, Philadelphia, PA 19104-6168, USA
| | - Terence P Speed
- Department of Statistics, University of California, Berkeley, Department of Mathematics and Statistics, The University of Melbourne, Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Australia
| | - Marcelo A Wood
- University of California, Irvine, Department of Neurobiology and Behavior, USA
| | - Ted Abel
- Department of Biology, University of Pennsylvania, Smilow Center for Translational Research, Room 10-170, Building 421, 3400 Civic Center Boulevard, Philadelphia, PA 19104-6168, USA
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50
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Maksimovic J, Gagnon-Bartsch JA, Speed TP, Oshlack A. Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data. Nucleic Acids Res 2015; 43:e106. [PMID: 25990733 PMCID: PMC4652745 DOI: 10.1093/nar/gkv526] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 05/09/2015] [Indexed: 12/28/2022] Open
Abstract
Due to their relatively low-cost per sample and broad, gene-centric coverage of CpGs across the human genome, Illumina's 450k arrays are widely used in large scale differential methylation studies. However, by their very nature, large studies are particularly susceptible to the effects of unwanted variation. The effects of unwanted variation have been extensively documented in gene expression array studies and numerous methods have been developed to mitigate these effects. However, there has been much less research focused on the appropriate methodology to use for accounting for unwanted variation in methylation array studies. Here we present a novel 2-stage approach using RUV-inverse in a differential methylation analysis of 450k data and show that it outperforms existing methods.
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Affiliation(s)
- Jovana Maksimovic
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia
| | | | - Terence P Speed
- Bioinformatics Division, Walter and Eliza Hall Institute, Parkville, VIC 3052, Australia Department of Statistics, University of California, Berkeley, CA 94705, USA Department of Mathematics and Statistics, The University of Melbourne, VIC 3010, Australia
| | - Alicia Oshlack
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia Department of Genetics, The University of Melbourne, VIC 3010, Australia
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